# Import Statements

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

import string
import regex as re
import spacy

from nltk.corpus import stopwords
# nltk.download('stopwords')
# nltk.download('punkt')
from nltk import word_tokenize
from nltk import FreqDist

import warnings
warnings.filterwarnings('ignore')

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.naive_bayes import MultinomialNB, GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import recall_score, accuracy_score, f1_score, confusion_matrix, classification_report
from sklearn.utils import class_weight
from sklearn.pipeline import Pipeline
from sklearn.base import TransformerMixin
from sklearn import set_config

from PIL import Image
from wordcloud import WordCloud
from textwrap import wrap

import joblib

# Read in the DataFrame I created in the Data Collection notebook 
df = pd.read_csv('..\Data\cities_df', index_col=0)
df.head()
df.describe()

df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 28379 entries, 0 to 3693
Data columns (total 2 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Attraction  28379 non-null  object
 1   City        28379 non-null  object
dtypes: object(2)
memory usage: 665.1+ KB

df.describe()

df.shape

(28379, 2)

#No null values because I scraped everything myself. Just to double-check:
df.isna().sum()

Attraction    0
City          0
dtype: int64

df.duplicated().sum()

846

# Look at duplicates in one city
df[(df.duplicated()==True) & (df['City']=='London, United Kingdom')]

df[df['Attraction']=='Oxford City Full-Day Private Tour from London']

# What about the top attraction?
df[df['Attraction']=='Desert Safari Dubai']

df = df.drop_duplicates()

# df.to_csv('../Data/cities_cleaned')

display(df.City.unique())
print('Total Unique Cities:', len(df.City.unique()))

array(['London, United Kingdom', 'Paris, France', 'Crete, Greece',
       'Bali, Indonesia', 'Rome, Italy', 'Phuket, Thailand',
       'Sicily, Italy', 'Majorca, Balearic Islands', 'Barcelona, Spain',
       'Istanbul, Turkey', 'Goa, India', 'Dubai, United Arab Emirates'],
      dtype=object)

Total Unique Cities: 12

df.City.value_counts(normalize=True)

City
Bali, Indonesia                0.177823
Rome, Italy                    0.174990
Dubai, United Arab Emirates    0.123597
London, United Kingdom         0.099626
Paris, France                  0.096938
Istanbul, Turkey               0.081248
Sicily, Italy                  0.071986
Barcelona, Spain               0.066502
Phuket, Thailand               0.041260
Crete, Greece                  0.037010
Majorca, Balearic Islands      0.016489
Goa, India                     0.012530
Name: proportion, dtype: float64

cities = df.groupby('City').count()

cities.reset_index(inplace=True)

sorted_cities = cities.sort_values(by='Attraction', ascending=False)
sorted_cities

# Plot the class imbalance
plt.figure(figsize=(10,8))
sns.barplot(x='Attraction', y='City', data=sorted_cities)
plt.title('Attractions Per City')
plt.xticks(rotation=90)
plt.show()

# Create a list of stopwords
stopwords_list = stopwords.words('english')
stopwords_list += list(string.punctuation)

# Preview the list
stopwords_list[:5]

['i', 'me', 'my', 'myself', 'we']

# Save stopwords list for app
joblib.dump(stopwords_list, '../Data/stopwords_list')

['../Data/stopwords_list']

# Lowercase all words in each corpus
df['cleaned'] = df['Attraction'].apply(lambda x: x.lower())
df.head()

# Remove commas, hyphens, colons, and other punctuation
df['cleaned'] = df['cleaned'].apply(lambda x: re.sub('[%s]' % re.escape(string.punctuation), '', x))
df.head()

# Use regex to get rid of numbers 
df['cleaned'] = df['cleaned'].apply(lambda x: re.sub('\w*\d\w*','', x))
df.head(10)

# !python -m spacy download en

# Lemmatize the text using spacy
nlp = spacy.load('en_core_web_sm')

df['lemmatized'] = df['cleaned'].apply(lambda x: ' '.join(
                                    [token.lemma_ for token in list(nlp(x)) if (token.is_stop==False)]))
df.head(10)

# Group the corpora by city and join them
df_to_group = df[['City', 'lemmatized']]
df_grouped = df_to_group.groupby(by='City').agg(lambda x:' '.join(x))
df_grouped

# Save grouped df
df_grouped.to_csv('../Data/df_grouped')

# Create a document term matrix using count vectorization
# Using count vectorization (most simple way to vectorize)
cv = CountVectorizer(analyzer='word', stop_words=stopwords_list)
data = cv.fit_transform(df_grouped['lemmatized'])
df_dtm = pd.DataFrame(data.toarray(), columns=cv.get_feature_names_out())
df_dtm.index = df_grouped.index
df_dtm

# Create a document term matrix using TF-IDF vectorization
# Might be good for classifying cities
tfidf = TfidfVectorizer(analyzer='word', stop_words=stopwords_list)
data2 = tfidf.fit_transform(df_grouped['lemmatized'])
df_dtm2 = pd.DataFrame(data2.toarray(), columns=tfidf.get_feature_names_out())
df_dtm2.index = df_grouped.index
df_dtm2

def generate_wordcloud(data, title):
    cloud = WordCloud(width=400, height=330, max_words=150, colormap='tab20c').generate_from_frequencies(data)
    plt.figure(figsize=(10,8))
    plt.imshow(cloud, interpolation='bilinear')
    plt.axis('off')
    plt.title('\n'.join(wrap(title,60)), fontsize=13)
    plt.show()

# Transposing document term matrix
df_dtm = df_dtm.transpose()

# Plotting word cloud for each city
for index, city in enumerate(df_dtm.columns):
    generate_wordcloud(df_dtm[city].sort_values(ascending=False), city)

# Look at top words with count vectorizer (in total, not per city)
sum_words = data.sum(axis=0)
words_freq = [(word, sum_words[0, idx]) for word, idx in cv.vocabulary_.items()]
words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)
words_freq

[('tour', 13527),
 ('private', 8730),
 ('transfer', 3613),
 ('day', 3533),
 ('airport', 2945),
 ('rome', 2814),
 ('bali', 2359),
 ('dubai', 2331),
 ('city', 2130),
 ('london', 2075),
 ('paris', 1871),
 ('guide', 1602),
 ('istanbul', 1473),
 ('barcelona', 1244),
 ('trip', 1153),
 ('safari', 1056),
 ('dinner', 992),
 ('desert', 967),
 ('ubud', 926),
 ('cruise', 923),
 ('walk', 921),
 ('experience', 884),
 ('ticket', 847),
 ('museum', 817),
 ('hotel', 796),
 ('lunch', 788),
 ('group', 730),
 ('good', 715),
 ('local', 713),
 ('colosseum', 698),
 ('vatican', 696),
 ('phuket', 644),
 ('class', 642),
 ('food', 638),
 ('fullday', 635),
 ('car', 630),
 ('island', 624),
 ('ride', 624),
 ('temple', 619),
 ('bike', 589),
 ('wine', 587),
 ('small', 582),
 ('night', 582),
 ('include', 579),
 ('taste', 576),
 ('line', 554),
 ('hour', 545),
 ('luxury', 536),
 ('skip', 528),
 ('adventure', 495),
 ('half', 478),
 ('palermo', 473),
 ('waterfall', 446),
 ('abu', 434),
 ('sightseeing', 431),
 ('dhabi', 431),
 ('highlight', 423),
 ('port', 423),
 ('excursion', 421),
 ('boat', 419),
 ('sunset', 418),
 ('visit', 397),
 ('water', 393),
 ('roman', 392),
 ('beach', 376),
 ('palace', 375),
 ('driver', 373),
 ('chapel', 370),
 ('arrival', 369),
 ('dune', 368),
 ('package', 366),
 ('morning', 359),
 ('sistine', 356),
 ('camel', 355),
 ('bbq', 355),
 ('quad', 354),
 ('park', 341),
 ('phi', 334),
 ('taormina', 334),
 ('skiptheline', 332),
 ('etna', 331),
 ('departure', 329),
 ('cooking', 327),
 ('catania', 326),
 ('sunrise', 325),
 ('vip', 321),
 ('river', 316),
 ('walking', 314),
 ('batur', 311),
 ('cappadocia', 308),
 ('de', 307),
 ('village', 301),
 ('evening', 299),
 ('smallgroup', 299),
 ('forum', 293),
 ('raft', 291),
 ('family', 291),
 ('civitavecchia', 288),
 ('home', 279),
 ('halfday', 277),
 ('explore', 275),
 ('lot', 272),
 ('nusa', 271),
 ('atv', 267),
 ('trek', 265),
 ('ancient', 265),
 ('castle', 265),
 ('tanah', 260),
 ('art', 260),
 ('burj', 260),
 ('shore', 257),
 ('st', 257),
 ('market', 253),
 ('old', 253),
 ('heathrow', 253),
 ('mount', 251),
 ('center', 249),
 ('goa', 248),
 ('versa', 245),
 ('street', 243),
 ('swing', 240),
 ('rice', 237),
 ('vice', 236),
 ('exclusive', 233),
 ('crete', 231),
 ('tower', 230),
 ('volcano', 229),
 ('amazing', 229),
 ('turkey', 224),
 ('mallorca', 224),
 ('discover', 223),
 ('chania', 219),
 ('service', 216),
 ('uluwatu', 214),
 ('penida', 213),
 ('red', 212),
 ('ephesus', 212),
 ('access', 210),
 ('louvre', 209),
 ('hill', 207),
 ('pickup', 204),
 ('fiumicino', 204),
 ('entrance', 199),
 ('terrace', 198),
 ('bus', 196),
 ('bosphorus', 190),
 ('van', 189),
 ('photo', 185),
 ('heraklion', 185),
 ('khalifa', 185),
 ('pick', 182),
 ('pasta', 182),
 ('premium', 179),
 ('traditional', 177),
 ('kintamani', 177),
 ('dance', 176),
 ('live', 176),
 ('garden', 176),
 ('kid', 176),
 ('gate', 175),
 ('central', 175),
 ('east', 173),
 ('blue', 171),
 ('tiramisu', 171),
 ('minivan', 167),
 ('world', 166),
 ('photographer', 165),
 ('pamukkale', 164),
 ('snorkeling', 163),
 ('al', 163),
 ('cesarinas', 163),
 ('palatine', 162),
 ('charter', 160),
 ('windsor', 160),
 ('hot', 157),
 ('cdg', 156),
 ('royal', 155),
 ('secret', 154),
 ('share', 153),
 ('marina', 152),
 ('palma', 152),
 ('villa', 149),
 ('tasting', 149),
 ('white', 148),
 ('jungle', 148),
 ('free', 148),
 ('way', 148),
 ('history', 148),
 ('mosque', 148),
 ('area', 147),
 ('vehicle', 146),
 ('basilica', 146),
 ('combo', 145),
 ('dhow', 145),
 ('shopping', 142),
 ('professional', 142),
 ('heaven', 141),
 ('spa', 141),
 ('rental', 140),
 ('sagrada', 140),
 ('sea', 139),
 ('inclusive', 138),
 ('english', 138),
 ('afternoon', 137),
 ('pizza', 137),
 ('stonehenge', 137),
 ('sand', 136),
 ('familia', 136),
 ('taxi', 135),
 ('photography', 134),
 ('transport', 132),
 ('heritage', 132),
 ('fast', 131),
 ('bay', 130),
 ('trekking', 129),
 ('people', 129),
 ('sicily', 129),
 ('montserrat', 127),
 ('workshop', 126),
 ('enjoy', 125),
 ('culture', 124),
 ('admission', 123),
 ('classic', 123),
 ('roundtrip', 123),
 ('boarding', 123),
 ('bath', 122),
 ('pompeii', 122),
 ('coast', 121),
 ('segway', 120),
 ('business', 120),
 ('eiffel', 120),
 ('versaille', 120),
 ('speedboat', 119),
 ('cook', 118),
 ('montmartre', 118),
 ('instagram', 117),
 ('entry', 117),
 ('quarter', 117),
 ('balloon', 116),
 ('fun', 115),
 ('audio', 115),
 ('forest', 114),
 ('minute', 114),
 ('virtual', 114),
 ('town', 114),
 ('massage', 113),
 ('grand', 113),
 ('breakfast', 112),
 ('champagne', 112),
 ('speed', 111),
 ('drive', 110),
 ('balinese', 110),
 ('view', 110),
 ('cycling', 109),
 ('option', 109),
 ('james', 109),
 ('lempuyang', 108),
 ('underground', 108),
 ('messina', 107),
 ('la', 106),
 ('bash', 106),
 ('bond', 106),
 ('ayung', 104),
 ('new', 104),
 ('expert', 104),
 ('train', 104),
 ('ciampino', 104),
 ('ski', 103),
 ('tapas', 103),
 ('spring', 102),
 ('south', 102),
 ('north', 101),
 ('sailing', 101),
 ('romantic', 100),
 ('round', 99),
 ('course', 99),
 ('flight', 99),
 ('optional', 99),
 ('chauffeur', 98),
 ('ebike', 98),
 ('sitia', 98),
 ('west', 97),
 ('buggy', 97),
 ('yacht', 97),
 ('valley', 97),
 ('fountain', 97),
 ('catacomb', 97),
 ('lagoon', 96),
 ('monkey', 96),
 ('buffet', 96),
 ('electric', 96),
 ('early', 96),
 ('charles', 96),
 ('seine', 96),
 ('ferrari', 95),
 ('natural', 94),
 ('hide', 94),
 ('max', 94),
 ('peters', 94),
 ('wifi', 93),
 ('historical', 93),
 ('cultural', 93),
 ('session', 93),
 ('semiprivate', 93),
 ('fco', 93),
 ('diving', 92),
 ('rafting', 92),
 ('travel', 92),
 ('french', 92),
 ('westminster', 92),
 ('escape', 91),
 ('jewish', 91),
 ('southampton', 91),
 ('florence', 91),
 ('jsh', 90),
 ('countryside', 89),
 ('open', 88),
 ('lake', 88),
 ('pass', 87),
 ('special', 86),
 ('photoshoot', 86),
 ('di', 86),
 ('make', 85),
 ('floor', 85),
 ('orly', 85),
 ('syracuse', 85),
 ('jeep', 83),
 ('mountain', 83),
 ('express', 83),
 ('winery', 83),
 ('oxford', 83),
 ('nature', 82),
 ('black', 82),
 ('shuttle', 82),
 ('gallery', 82),
 ('activity', 81),
 ('house', 81),
 ('tivoli', 81),
 ('fishing', 80),
 ('beautiful', 80),
 ('gaulle', 80),
 ('amalfi', 80),
 ('agrigento', 80),
 ('coffee', 79),
 ('cave', 79),
 ('track', 79),
 ('show', 79),
 ('turkish', 79),
 ('spot', 78),
 ('jatiluwih', 78),
 ('cefalù', 78),
 ('cta', 78),
 ('semi', 77),
 ('time', 77),
 ('harry', 77),
 ('authentic', 76),
 ('camp', 76),
 ('site', 76),
 ('hidden', 75),
 ('hike', 75),
 ('gatwick', 75),
 ('air', 74),
 ('kecak', 74),
 ('big', 74),
 ('tofrom', 74),
 ('jet', 73),
 ('transportation', 73),
 ('sight', 73),
 ('arena', 73),
 ('sicilian', 73),
 ('modern', 72),
 ('pantheon', 72),
 ('snorkel', 71),
 ('fromto', 71),
 ('plane', 71),
 ('scuba', 70),
 ('game', 70),
 ('historic', 70),
 ('christmas', 70),
 ('layover', 70),
 ('dxb', 70),
 ('potter', 70),
 ('mt', 69),
 ('land', 69),
 ('gourmet', 69),
 ('abbey', 69),
 ('discovery', 68),
 ('gorge', 68),
 ('italian', 68),
 ('pmo', 68),
 ('daily', 67),
 ('canoe', 67),
 ('district', 67),
 ('le', 67),
 ('sabiha', 67),
 ('san', 66),
 ('cathedral', 66),
 ('rethymno', 66),
 ('sophia', 66),
 ('disneyland', 66),
 ('hire', 65),
 ('fire', 65),
 ('light', 65),
 ('treasure', 65),
 ('centre', 65),
 ('plus', 65),
 ('gothic', 65),
 ('nga', 65),
 ('hunt', 64),
 ('vacation', 64),
 ('room', 64),
 ('station', 64),
 ('hagia', 64),
 ('phang', 64),
 ('lesson', 63),
 ('customize', 63),
 ('international', 63),
 ('costa', 63),
 ('british', 63),
 ('unique', 62),
 ('friendly', 62),
 ('accommodation', 62),
 ('brava', 62),
 ('knossos', 62),
 ('square', 62),
 ('khai', 62),
 ('noto', 62),
 ('dps', 61),
 ('horse', 61),
 ('vintage', 61),
 ('heart', 61),
 ('creta', 61),
 ('trastevere', 61),
 ('allinclusive', 60),
 ('gaudi', 60),
 ('arab', 60),
 ('creek', 60),
 ('belly', 60),
 ('monreale', 60),
 ('club', 59),
 ('ultimate', 59),
 ('love', 59),
 ('thai', 59),
 ('pmi', 59),
 ('kuta', 58),
 ('chef', 58),
 ('panoramic', 58),
 ('min', 58),
 ('medieval', 58),
 ('giverny', 58),
 ('executive', 57),
 ('luton', 57),
 ('saint', 57),
 ('tuscany', 57),
 ('sport', 56),
 ('jimbaran', 55),
 ('pax', 55),
 ('meal', 55),
 ('rock', 55),
 ('hopon', 55),
 ('catamaran', 55),
 ('sandboarde', 55),
 ('zaye', 55),
 ('church', 55),
 ('normandy', 55),
 ('castel', 55),
 ('fly', 54),
 ('rai', 54),
 ('bedugul', 54),
 ('attraction', 54),
 ('drop', 54),
 ('scooter', 54),
 ('dining', 54),
 ('drink', 54),
 ('coral', 54),
 ('cheese', 54),
 ('hopoff', 54),
 ('chq', 54),
 ('loire', 54),
 ('telaga', 53),
 ('chocolate', 53),
 ('golf', 53),
 ('shoot', 53),
 ('customer', 53),
 ('hop', 53),
 ('italy', 53),
 ('gokcen', 53),
 ('ngurah', 52),
 ('plantation', 52),
 ('denpasar', 52),
 ('gem', 52),
 ('place', 52),
 ('terminal', 52),
 ('trevi', 52),
 ('orvieto', 52),
 ('dua', 51),
 ('waja', 51),
 ('tirta', 51),
 ('overnight', 51),
 ('star', 51),
 ('hiking', 50),
 ('besakih', 50),
 ('national', 50),
 ('real', 50),
 ('atlantis', 50),
 ('sheikh', 50),
 ('unesco', 49),
 ('cepung', 49),
 ('iconic', 49),
 ('aquarium', 49),
 ('official', 49),
 ('cretan', 49),
 ('frame', 49),
 ('latin', 49),
 ('topkapi', 49),
 ('cabaret', 49),
 ('oil', 48),
 ('journey', 48),
 ('summit', 48),
 ('girona', 48),
 ('cab', 48),
 ('dover', 48),
 ('borghese', 48),
 ('sorrento', 48),
 ('great', 47),
 ('location', 47),
 ('cotswold', 47),
 ('lhr', 47),
 ('piazza', 47),
 ('tukad', 46),
 ('self', 46),
 ('dive', 46),
 ('return', 46),
 ('pub', 46),
 ('spanish', 46),
 ('bcn', 46),
 ('independent', 46),
 ('antalya', 46),
 ('baroque', 46),
 ('like', 45),
 ('vespa', 45),
 ('peter', 45),
 ('deste', 45),
 ('ostia', 45),
 ('godfather', 45),
 ('seminyak', 44),
 ('custom', 44),
 ('magic', 44),
 ('bazaar', 44),
 ('ory', 44),
 ('ghetto', 44),
 ('siracusa', 44),
 ('point', 43),
 ('kayak', 43),
 ('troy', 43),
 ('trapani', 43),
 ('rent', 42),
 ('sedan', 42),
 ('selfguide', 42),
 ('miracle', 42),
 ('vineyard', 42),
 ('appian', 42),
 ('restaurant', 41),
 ('riding', 41),
 ('offer', 41),
 ('withlocal', 41),
 ('tea', 41),
 ('high', 41),
 ('passenger', 41),
 ('personalize', 41),
 ('olive', 41),
 ('ist', 41),
 ('churchill', 41),
 ('koh', 41),
 ('castelmola', 41),
 ('personal', 40),
 ('surf', 40),
 ('wonderful', 40),
 ('wonder', 40),
 ('holiday', 40),
 ('online', 40),
 ('stop', 40),
 ('sitge', 40),
 ('viceversa', 40),
 ('wild', 40),
 ('gelato', 40),
 ('gallipoli', 40),
 ('war', 40),
 ('ortigia', 40),
 ('lembongan', 39),
 ('vw', 39),
 ('canyon', 39),
 ('magical', 39),
 ('ghost', 39),
 ('see', 39),
 ('mercede', 39),
 ('entertainment', 39),
 ('canal', 39),
 ('bursa', 39),
 ('kidfriendly', 39),
 ('parisian', 39),
 ('orsay', 39),
 ('modica', 39),
 ('erice', 39),
 ('minibus', 38),
 ('yoga', 38),
 ('mini', 38),
 ('horseback', 38),
 ('wadi', 38),
 ('sharjah', 38),
 ('islands', 38),
 ('ragusa', 38),
 ('famous', 37),
 ('trail', 37),
 ('learn', 37),
 ('el', 37),
 ('golden', 37),
 ('ottoman', 37),
 ('court', 37),
 ('assisi', 37),
 ('dolphin', 36),
 ('speaking', 36),
 ('bird', 36),
 ('eye', 36),
 ('basis', 36),
 ('postcode', 36),
 ('dday', 36),
 ('marais', 36),
 ('racha', 36),
 ('positano', 36),
 ('navona', 36),
 ('alcantara', 36),
 ('safe', 35),
 ('incl', 35),
 ('rover', 35),
 ('del', 35),
 ('casa', 35),
 ('outlet', 35),
 ('archaeological', 35),
 ('krabi', 35),
 ('roma', 35),
 ('papal', 35),
 ('naples', 35),
 ('beginner', 34),
 ('holy', 34),
 ('honeymoon', 34),
 ('treatment', 34),
 ('beauty', 34),
 ('danu', 34),
 ('complete', 34),
 ('spice', 34),
 ('western', 34),
 ('helicopter', 34),
 ('monastery', 34),
 ('region', 34),
 ('global', 34),
 ('mediterranean', 34),
 ('audioguide', 34),
 ('path', 34),
 ('music', 34),
 ('bridge', 34),
 ('naxos', 34),
 ('person', 33),
 ('exploration', 33),
 ('seafood', 33),
 ('shop', 33),
 ('palm', 33),
 ('host', 33),
 ('platform', 33),
 ('hampton', 33),
 ('pastry', 33),
 ('simon', 33),
 ('gladiator', 33),
 ('ulun', 32),
 ('mystery', 32),
 ('meet', 32),
 ('price', 32),
 ('daytrip', 32),
 ('priority', 32),
 ('cava', 32),
 ('welcome', 32),
 ('story', 32),
 ('mall', 32),
 ('emirate', 32),
 ('jack', 32),
 ('siena', 32),
 ('cia', 32),
 ('scenic', 31),
 ('sekumpul', 31),
 ('sanur', 31),
 ('life', 31),
 ('bamboo', 31),
 ('zipline', 31),
 ('lose', 31),
 ('hrs', 31),
 ('hr', 31),
 ('landmark', 31),
 ('musée', 31),
 ('tegalalang', 30),
 ('field', 30),
 ('canggu', 30),
 ('crater', 30),
 ('cocktail', 30),
 ('santa', 30),
 ('footstep', 30),
 ('christian', 30),
 ('venice', 30),
 ('pauls', 30),
 ('da', 30),
 ('empul', 29),
 ('green', 29),
 ('destination', 29),
 ('seat', 29),
 ('studio', 29),
 ('foodie', 29),
 ('bicycle', 29),
 ('step', 29),
 ('france', 29),
 ('samaria', 29),
 ('accessible', 29),
 ('zoo', 29),
 ('immersive', 29),
 ('guard', 29),
 ('tegenungan', 28),
 ('route', 28),
 ('body', 28),
 ('well', 28),
 ('relax', 28),
 ('farm', 28),
 ('organic', 28),
 ('gangga', 28),
 ('country', 28),
 ('picnic', 28),
 ('eat', 28),
 ('prince', 28),
 ('gold', 28),
 ('guell', 28),
 ('license', 28),
 ('cruiser', 28),
 ('chamber', 28),
 ('warner', 28),
 ('pier', 28),
 ('museums', 28),
 ('mont', 28),
 ('dorsay', 28),
 ('segesta', 28),
 ('giardini', 28),
 ('tibumana', 27),
 ('padi', 27),
 ('combination', 27),
 ('taman', 27),
 ('diver', 27),
 ('super', 27),
 ('tubing', 27),
 ('road', 27),
 ('beer', 27),
 ('climb', 27),
 ('end', 27),
 ('flamenco', 27),
 ('europe', 27),
 ('rooftop', 27),
 ('sant', 27),
 ('mustsee', 27),
 ('theme', 27),
 ('agia', 27),
 ('arabian', 27),
 ('bros', 27),
 ('stopover', 27),
 ('byzantine', 27),
 ('imperial', 27),
 ('movie', 27),
 ('cart', 27),
 ('frascati', 27),
 ('fish', 26),
 ('board', 26),
 ('sky', 26),
 ('deep', 26),
 ('ruin', 26),
 ('gaudí', 26),
 ('güell', 26),
 ('stadium', 26),
 ('img', 26),
 ('disposal', 26),
 ('asian', 26),
 ('thame', 26),
 ('greenwich', 26),
 ('soho', 26),
 ('hanuman', 26),
 ('raya', 26),
 ('gili', 25),
 ('change', 25),
 ('cliff', 25),
 ('itinerary', 25),
 ('lovina', 25),
 ('eastern', 25),
 ('eco', 25),
 ('couple', 25),
 ('dropoff', 25),
 ('crawl', 25),
 ('culinary', 25),
 ('original', 25),
 ('andorra', 25),
 ('legend', 25),
 ('architecture', 25),
 ('crypt', 25),
 ('legoland', 25),
 ('anzac', 25),
 ('kensington', 25),
 ('cambridge', 25),
 ('lcy', 25),
 ('monet', 25),
 ('fashion', 25),
 ('moulin', 25),
 ('rouge', 25),
 ('pisa', 25),
 ('antica', 25),
 ('marsala', 25),
 ('capo', 25),
 ('barong', 24),
 ('underwater', 24),
 ('standard', 24),
 ('ijen', 24),
 ('true', 24),
 ('daytour', 24),
 ('resort', 24),
 ('level', 24),
 ('start', 24),
 ('reality', 24),
 ('party', 24),
 ('cable', 24),
 ('mosaic', 24),
 ('beat', 24),
 ('cellar', 24),
 ('rail', 24),
 ('opera', 24),
 ('gardens', 24),
 ('uae', 24),
 ('reserve', 24),
 ('continent', 24),
 ('dolmabahce', 24),
 ('michel', 24),
 ('maiton', 24),
 ('campo', 24),
 ('terracina', 24),
 ('banana', 23),
 ('tanjung', 23),
 ('certify', 23),
 ('healing', 23),
 ('handara', 23),
 ('unforgettable', 23),
 ('sacred', 23),
 ('direct', 23),
 ('bar', 23),
 ('main', 23),
 ('instagramable', 23),
 ('easy', 23),
 ('stay', 23),
 ('wildlife', 23),
 ('lover', 23),
 ('kickstart', 23),
 ('sail', 23),
 ('royalty', 23),
 ('relic', 23),
 ('picasso', 23),
 ('balos', 23),
 ('selfdrive', 23),
 ('bodrum', 23),
 ('battlefield', 23),
 ('beatle', 23),
 ('tate', 23),
 ('shakespeare', 23),
 ('khao', 23),
 ('gimignano', 23),
 ('civita', 23),
 ('paradise', 22),
 ('float', 22),
 ('pool', 22),
 ('mother', 22),
 ('maya', 22),
 ('escooter', 22),
 ('tree', 22),
 ('salt', 22),
 ('sanctuary', 22),
 ('style', 22),
 ('paella', 22),
 ('montjuic', 22),
 ('voicemap', 22),
 ('agio', 22),
 ('nikolaos', 22),
 ('elafonisi', 22),
 ('greek', 22),
 ('ripper', 22),
 ('buckingham', 22),
 ('notre', 22),
 ('fiori', 22),
 ('santangelo', 22),
 ('stpeter', 22),
 ('pope', 22),
 ('bagnoregio', 22),
 ('cesarina', 22),
 ('armerina', 22),
 ('individual', 21),
 ('tulamben', 21),
 ('watch', 21),
 ('camping', 21),
 ('extreme', 21),
 ('southern', 21),
 ('try', 21),
 ('john', 21),
 ('vr', 21),
 ('historian', 21),
 ('santorini', 21),
 ('making', 21),
 ('sapanca', 21),
 ('lgw', 21),
 ('stanste', 21),
 ('cala', 21),
 ('marai', 21),
 ('roissy', 21),
 ('dame', 21),
 ('aperitivo', 21),
 ('pompei', 21),
 ('vulcano', 20),
 ('banyumala', 20),
 ('virgin', 20),
 ('volkswagen', 20),
 ('melangit', 20),
 ('ayun', 20),
 ('saver', 20),
 ('tourist', 20),
 ('magnificent', 20),
 ('butterfly', 20),
 ('capital', 20),
 ('delivery', 20),
 ('long', 20),
 ('week', 20),
 ('flavor', 20),
 ('elounda', 20),
 ('aquaventure', 20),
 ('essential', 20),
 ('horn', 20),
 ('vito', 20),
 ('notte', 20),
 ('salisbury', 20),
 ('fontainebleau', 20),
 ('hong', 20),
 ('paraglide', 19),
 ('popular', 19),
 ('child', 19),
 ('create', 19),
 ('driving', 19),
 ('king', 19),
 ('coach', 19),
 ('unlimited', 19),
 ('near', 19),
 ('outdoor', 19),
 ('pack', 19),
 ('skyline', 19),
 ('ultra', 19),
 ('gramvousa', 19),
 ('jetski', 19),
 ('stanbul', 19),
 ('kusadasi', 19),
 ('camden', 19),
 ('downton', 19),
 ('blenheim', 19),
 ('ltn', 19),
 ('chantilly', 19),
 ('landing', 19),
 ('raphael', 19),
 ('spiritual', 18),
 ('walker', 18),
 ('combine', 18),
 ('incredible', 18),
 ('menjangan', 18),
 ('charm', 18),
 ('artisan', 18),
 ('school', 18),
 ('spectacular', 18),
 ('padang', 18),
 ('paddle', 18),
 ('break', 18),
 ('explorer', 18),
 ('introduction', 18),
 ('nou', 18),
 ('bear', 18),
 ('aperitif', 18),
 ('prat', 18),
 ('gastronomy', 18),
 ('year', 18),
 ('lo', 18),
 ('public', 18),
 ('typical', 18),
 ('jumeirah', 18),
 ('deluxe', 18),
 ('bollywood', 18),
 ('escort', 18),
 ('istanbuls', 18),
 ('tomb', 18),
 ('film', 18),
 ...]

# Transposing document term matrix
df_dtm2 = df_dtm2.transpose()

# Plotting word cloud for each city
for index, city in enumerate(df_dtm2.columns):
    generate_wordcloud(df_dtm2[city].sort_values(ascending=False), city)

# Look at top words with tf-idf vectorization (for total words, not per city)
sum_words = data2.sum(axis=0)
words_freq = [(word, sum_words[0, idx]) for word, idx in tfidf.vocabulary_.items()]
words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)
words_freq

[('tour', 3.138098562201453),
 ('private', 2.0825423088575428),
 ('transfer', 0.9858659955017881),
 ('goa', 0.9237376001392338),
 ('barcelona', 0.916553640718507),
 ('london', 0.8927533459748753),
 ('paris', 0.8822607586615051),
 ('istanbul', 0.8681465446126231),
 ('airport', 0.8578475408738101),
 ('dubai', 0.8396210065107451),
 ('day', 0.8337294625779452),
 ('phuket', 0.8033367684025245),
 ('mallorca', 0.7490675159701988),
 ('bali', 0.7376600455046178),
 ('rome', 0.6629694357497168),
 ('palma', 0.5082958144083493),
 ('crete', 0.4738788186359383),
 ('palermo', 0.4686929630407816),
 ('city', 0.4658936476639842),
 ('chania', 0.4492617371483571),
 ('phi', 0.4166373923081416),
 ('guide', 0.40388686280511066),
 ('taormina', 0.38589119407720635),
 ('etna', 0.3824251055076506),
 ('heraklion', 0.37951333960021033),
 ('ubud', 0.37703781127932107),
 ('trip', 0.3233023886377564),
 ('catania', 0.32283382386081877),
 ('island', 0.3070577380372553),
 ('desert', 0.3020386322104823),
 ('colosseum', 0.28944923808256634),
 ('vatican', 0.2886198706382037),
 ('walk', 0.24365987791520896),
 ('de', 0.23933024676314693),
 ('experience', 0.2319534359719405),
 ('cruise', 0.21618352115813164),
 ('lunch', 0.21333061964046546),
 ('wine', 0.21188758401881197),
 ('local', 0.2048278137423077),
 ('safari', 0.20369673918616388),
 ('sitia', 0.20103949881524655),
 ('pmi', 0.19729903322429343),
 ('jsh', 0.1846281111568591),
 ('boat', 0.1817521982843795),
 ('cappadocia', 0.18152690817426195),
 ('group', 0.17828984844031226),
 ('hotel', 0.17599974009961786),
 ('dinner', 0.1733772077969627),
 ('taste', 0.17184544657238202),
 ('beach', 0.16892293673888853),
 ('food', 0.1657113173510083),
 ('museum', 0.16320814596959837),
 ('class', 0.16157583542428305),
 ('ticket', 0.15961308835129107),
 ('abu', 0.15632583304404266),
 ('dhabi', 0.15524523972807),
 ('temple', 0.15108841765694975),
 ('good', 0.14991260699179831),
 ('fullday', 0.14907451241799458),
 ('sistine', 0.14762740509655248),
 ('car', 0.14649481120930058),
 ('waterfall', 0.14266739539813184),
 ('small', 0.14212310867441014),
 ('excursion', 0.1397579935608121),
 ('bike', 0.13626248392244555),
 ('rethymno', 0.13539394818169667),
 ('dune', 0.13255278009264446),
 ('turkey', 0.13201956958128142),
 ('include', 0.12947210752955635),
 ('knossos', 0.12718825435250292),
 ('hour', 0.12666415091744057),
 ('batur', 0.1266293297061219),
 ('heathrow', 0.12656693961957438),
 ('sicily', 0.12609052489001765),
 ('luxury', 0.12530136833167274),
 ('creta', 0.1251368308952045),
 ('ephesus', 0.12494709263942706),
 ('messina', 0.12362382564748826),
 ('forum', 0.12150233059912886),
 ('sightseeing', 0.12081825578971062),
 ('half', 0.12028204794221654),
 ('night', 0.1198605068540515),
 ('civitavecchia', 0.11942891198822224),
 ('line', 0.1191687180759041),
 ('adventure', 0.11866336182246942),
 ('chapel', 0.11840169286263912),
 ('skip', 0.11340531581019436),
 ('bosphorus', 0.11198088491269408),
 ('chq', 0.11077686669411546),
 ('ride', 0.1105113947721325),
 ('nusa', 0.1103425991972959),
 ('palace', 0.10909412759248191),
 ('bay', 0.1066070272162302),
 ('atv', 0.10650415161126074),
 ('tanah', 0.10586374830736876),
 ('port', 0.10391251644646027),
 ('roman', 0.10337262748282978),
 ('sagrada', 0.10314912355352972),
 ('visit', 0.10108895816950264),
 ('cretan', 0.10051974940762327),
 ('sunset', 0.10031004474575289),
 ('familia', 0.10020200573771458),
 ('syracuse', 0.09820584280407947),
 ('water', 0.09809938595422275),
 ('swing', 0.0977203830529558),
 ('cdg', 0.09718676221536458),
 ('highlight', 0.09668623444288184),
 ('pamukkale', 0.09665718487200962),
 ('lot', 0.09575574498830418),
 ('old', 0.09513897938071034),
 ('cooking', 0.09508255139014962),
 ('burj', 0.09365142071762922),
 ('montserrat', 0.09357099065213052),
 ('james', 0.09275321738352545),
 ('arrival', 0.09262102690041997),
 ('agrigento', 0.09242902852148654),
 ('mount', 0.09222456436373663),
 ('versa', 0.09193471020794969),
 ('bond', 0.09161465991980078),
 ('raft', 0.0914946585743993),
 ('louvre', 0.09052463772011213),
 ('park', 0.0902286706257994),
 ('cta', 0.09011830280844939),
 ('castle', 0.08961986581289814),
 ('vice', 0.0892880904254558),
 ('quad', 0.08928309829030198),
 ('camel', 0.08836365880525354),
 ('walking', 0.08799472018866634),
 ('village', 0.08726308193527141),
 ('uluwatu', 0.08713400822221891),
 ('penida', 0.08672683995949826),
 ('departure', 0.08609987563389516),
 ('plantation', 0.08557034536354162),
 ('cesarinas', 0.08476639867960487),
 ('fiumicino', 0.08459547932499076),
 ('sicilian', 0.08434148852585648),
 ('tiramisu', 0.08189054566104409),
 ('speedboat', 0.08113455453055947),
 ('nga', 0.08108212724559642),
 ('north', 0.08066114308307175),
 ('home', 0.08064333483861291),
 ('windsor', 0.08004233335625256),
 ('phang', 0.07983470990335648),
 ('skiptheline', 0.07977883762436667),
 ('vip', 0.07880757050025558),
 ('pmo', 0.07856467424326356),
 ('khai', 0.07733987521887659),
 ('shore', 0.07657412667103831),
 ('cefalù', 0.0754863337459489),
 ('gorge', 0.0751395863661337),
 ('versaille', 0.07475904785797276),
 ('south', 0.07382887210508818),
 ('tower', 0.07356086888406942),
 ('montmartre', 0.07351306372700654),
 ('driver', 0.07326392285259319),
 ('kintamani', 0.0720687825015549),
 ('explore', 0.07204851665980237),
 ('noto', 0.07163249710415208),
 ('river', 0.07078409212684715),
 ('cala', 0.07022507962220614),
 ('customer', 0.07015231538566796),
 ('pasta', 0.07005931165573354),
 ('volcano', 0.06998314269752369),
 ('halfday', 0.06992041559812033),
 ('art', 0.06975107863852788),
 ('st', 0.06964905694304915),
 ('sunrise', 0.06959244427840715),
 ('sailing', 0.06944917812016062),
 ('monreale', 0.06932177139111492),
 ('stonehenge', 0.06853624793629126),
 ('dudhsagar', 0.06805384182361718),
 ('bbq', 0.06791800870437857),
 ('rice', 0.0677973632353311),
 ('morning', 0.06771566302617796),
 ('market', 0.06730870319352147),
 ('center', 0.06719598617682139),
 ('smallgroup', 0.06689384872300673),
 ('khalifa', 0.06663658781831311),
 ('spice', 0.06656519584913641),
 ('package', 0.06648647513261614),
 ('speed', 0.06624223924595612),
 ('eiffel', 0.06388773935086059),
 ('rental', 0.06382563840199988),
 ('family', 0.06302416151249232),
 ('heritage', 0.06288475584868775),
 ('discover', 0.06271979930868835),
 ('india', 0.06265113390420263),
 ('tapas', 0.0626020004541849),
 ('jungle', 0.06223544883355017),
 ('segway', 0.06206844346777614),
 ('cave', 0.060998525306394935),
 ('trek', 0.060611163169160404),
 ('palatine', 0.059602213550173665),
 ('samaria', 0.059491280261654594),
 ('snorkeling', 0.05913697620264416),
 ('ancient', 0.05847647403916104),
 ('canoe', 0.05844324014657467),
 ('access', 0.05739773107789663),
 ('terrace', 0.057348316825488396),
 ('premium', 0.05676393058285566),
 ('catamaran', 0.056529062263350034),
 ('street', 0.055801653547307234),
 ('coral', 0.05568516418976243),
 ('mosque', 0.05562354131056334),
 ('agia', 0.05538843334705773),
 ('town', 0.05491839335066291),
 ('hampi', 0.054443073458893744),
 ('sea', 0.05431132289147468),
 ('van', 0.05422884510474162),
 ('charter', 0.05356835821674179),
 ('exclusive', 0.05334157804062066),
 ('area', 0.05305583807708972),
 ('orly', 0.05295432556606403),
 ('east', 0.05263077743881874),
 ('service', 0.052468081038847064),
 ('villa', 0.052279251662202486),
 ('dhow', 0.05222867693867784),
 ('godfather', 0.05199132854333619),
 ('pickup', 0.05170536960980403),
 ('koh', 0.05114411103183775),
 ('seine', 0.05104695160838802),
 ('siracusa', 0.05083596568681761),
 ('charles', 0.05020375331104862),
 ('majorca', 0.05016077115871868),
 ('heaven', 0.05002661322870336),
 ('panaji', 0.0499061506706526),
 ('badami', 0.0499061506706526),
 ('evening', 0.049872590053577644),
 ('gaulle', 0.0498393652386485),
 ('valley', 0.049754145338676656),
 ('trapani', 0.04968060283029902),
 ('quarter', 0.04967018850816062),
 ('gate', 0.048739661196324884),
 ('diving', 0.04871450820978362),
 ('winery', 0.04871258953013535),
 ('balloon', 0.048689748931370416),
 ('red', 0.048659069294311116),
 ('electric', 0.048016348363087595),
 ('history', 0.0478688966508233),
 ('castelmola', 0.04736987711726186),
 ('thai', 0.047281654814117655),
 ('bus', 0.0472314089457581),
 ('balos', 0.04718273951786399),
 ('hill', 0.04682499889306569),
 ('pizza', 0.04675998968701412),
 ('ortigia', 0.04621451426074327),
 ('westminster', 0.046024341679845225),
 ('central', 0.04602109830388052),
 ('jeep', 0.045882930374400575),
 ('southampton', 0.045524077096368645),
 ('mumbai', 0.04536922788241145),
 ('agio', 0.04513131606056556),
 ('nikolaos', 0.04513131606056556),
 ('elafonisi', 0.04513131606056556),
 ('erice', 0.045059151404224694),
 ('di', 0.045030421168799975),
 ('photo', 0.04496484424693435),
 ('racha', 0.04490702432063802),
 ('scuba', 0.04473983651034698),
 ('trekking', 0.04463079173264452),
 ('cabaret', 0.04444957346472021),
 ('gaudi', 0.044206767237227024),
 ('amazing', 0.044131262512439685),
 ('pompeii', 0.04408459795594175),
 ('lempuyang', 0.04397417237383011),
 ('krabi', 0.043659606978398074),
 ('photographer', 0.043604911957418106),
 ('entrance', 0.04357972483757398),
 ('tasting', 0.04349845863260848),
 ('french', 0.04322463920243878),
 ('traditional', 0.04312749088040545),
 ('ciampino', 0.04312710710685803),
 ('santorini', 0.043079892603267125),
 ('olive', 0.042838507942500964),
 ('pick', 0.042352079612257953),
 ('ayung', 0.04234549932294751),
 ('secret', 0.042160115092809494),
 ('marina', 0.041850280971336515),
 ('basilica', 0.04172384711561687),
 ('alcantara', 0.04159306283466895),
 ('oxford', 0.04152196042855602),
 ('brava', 0.041469950165531844),
 ('kid', 0.041164909292155605),
 ('simon', 0.041164772293918184),
 ('disneyland', 0.04111747632188502),
 ('elounda', 0.041028469145968686),
 ('fishing', 0.041014668916903536),
 ('latin', 0.04088101975839714),
 ('business', 0.04086885127790192),
 ('panjim', 0.04083230509417031),
 ('al', 0.04081421908080346),
 ('dance', 0.04037619458885896),
 ('minivan', 0.040078512924272336),
 ('white', 0.03982496574285),
 ('kayak', 0.03965842837884587),
 ('sabiha', 0.039487996258686854),
 ('naxos', 0.03928233712163178),
 ('train', 0.03919079727840462),
 ('peters', 0.03898026988504475),
 ('gramvousa', 0.038977045688670255),
 ('sophia', 0.038898623180198995),
 ('balinese', 0.03874796563234792),
 ('fco', 0.03856558616286343),
 ('massage', 0.03824867811804962),
 ('bash', 0.03818096383103345),
 ('blue', 0.03811707512317787),
 ('snorkel', 0.038038703560186696),
 ('inclusive', 0.037869838798751825),
 ('hagia', 0.03771987702322327),
 ('culture', 0.03766777042461863),
 ('gatwick', 0.037519843760743396),
 ('professional', 0.03751062089960914),
 ('costa', 0.037509252828097674),
 ('spa', 0.03747454639684817),
 ('church', 0.03740464538399271),
 ('cook', 0.037222253376202614),
 ('coast', 0.037179360759407316),
 ('hike', 0.03704540766637057),
 ('west', 0.03694014797666641),
 ('champagne', 0.03688747297079222),
 ('drach', 0.0367845655163937),
 ('lagoon', 0.03657917327733942),
 ('garden', 0.036491897861613304),
 ('free', 0.03639551273010936),
 ('catacomb', 0.03632776261593659),
 ('live', 0.03630964750600018),
 ('divar', 0.03629538230592916),
 ('goan', 0.03629538230592916),
 ('hubli', 0.03629538230592916),
 ('giverny', 0.03613353979802016),
 ('gothic', 0.036065871309892424),
 ('sand', 0.035793190356862264),
 ('turkish', 0.03557801123052035),
 ('girona', 0.03536541378978162),
 ('bath', 0.03533154229084169),
 ('trail', 0.035326773497287446),
 ('la', 0.03530049944807213),
 ('ragusa', 0.03516066821992136),
 ('cycling', 0.035132044558901426),
 ('roundtrip', 0.03505622384048468),
 ('modica', 0.03488069957315188),
 ('hot', 0.03460725592318522),
 ('audio', 0.034570138647264284),
 ('yacht', 0.0344886050849099),
 ('royal', 0.034410432567374895),
 ('cultural', 0.034370591153695756),
 ('taxi', 0.034344473225900424),
 ('normandy', 0.03426456360157085),
 ('cathedral', 0.03411516801079608),
 ('platform', 0.03406945768545709),
 ('grand', 0.03406039266866741),
 ('yoga', 0.034056098128518064),
 ('escape', 0.03392033372166563),
 ('bcn', 0.03389185488187405),
 ('workshop', 0.03385788648222675),
 ('share', 0.033684368375287835),
 ('loire', 0.03364157153608774),
 ('tivoli', 0.033589381496687505),
 ('vehicle', 0.03346202831124101),
 ('pastilla', 0.03344051410581245),
 ('alcudia', 0.03344051410581245),
 ('harry', 0.033187143515708485),
 ('amalfi', 0.03317469777450618),
 ('florence', 0.0330444068292398),
 ('jet', 0.03278650133385952),
 ('islands', 0.032734500180503376),
 ('mountain', 0.032720803447477576),
 ('forest', 0.03263778273577361),
 ('oil', 0.03262133933118514),
 ('flight', 0.032610548601813685),
 ('combo', 0.032553721831980194),
 ('hanuman', 0.032432850898238566),
 ('raya', 0.032432850898238566),
 ('big', 0.03242329134666585),
 ('segesta', 0.03235015998252029),
 ('giardini', 0.03235015998252029),
 ('sport', 0.03206129241106248),
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 ('seabybus', 0.012308540743790607),
 ('palekastro', 0.012308540743790607),
 ('sissi', 0.012308540743790607),
 ('kalo', 0.012308540743790607),
 ('chorio', 0.012308540743790607),
 ('eloúnda', 0.012308540743790607),
 ('ruin', 0.01230610651040318),
 ('long', 0.01227764948412368),
 ('custom', 0.012249062203889365),
 ('tegalalang', 0.012215047881619475),
 ('canggu', 0.012215047881619475),
 ('sic', 0.012166207690899298),
 ('mercede', 0.012153675347718699),
 ('overnight', 0.012135177992892435),
 ('france', 0.012103125594661061),
 ('high', 0.012075501242374297),
 ('daytrip', 0.012065331874068708),
 ('demo', 0.012059201606218919),
 ('mystery', 0.012046902938730723),
 ('rediscover', 0.012045221173918172),
 ('entertainment', 0.012004878364804562),
 ('commercial', 0.011998428006977922),
 ('beauty', 0.01198648640559483),
 ('exploration', 0.011938452361506785),
 ('chantilly', 0.011836849244179019),
 ('greece', 0.011826897404699225),
 ('gelato', 0.011820439389438777),
 ('empul', 0.011807879618898825),
 ('near', 0.01179772549832075),
 ('crawl', 0.011796472043971098),
 ('figuere', 0.01178847126326054),
 ('horn', 0.01178746156975727),
 ('riding', 0.011733827922077084),
 ('spain', 0.011730476746687209),
 ('personalize', 0.01172370655162294),
 ('tree', 0.011718551490620059),
 ('whitewater', 0.011717499533321006),
 ('archaeologist', 0.011710211158179433),
 ('body', 0.011708197431080506),
 ('instagramable', 0.011589340334595315),
 ('cellar', 0.011582427098899317),
 ('dia', 0.01156295681060128),
 ('withlocal', 0.011557159011252599),
 ('casale', 0.011553628565185818),
 ('lipari', 0.011553628565185818),
 ('madonie', 0.011553628565185818),
 ('turchi', 0.011553628565185818),
 ('selinunte', 0.011553628565185818),
 ('online', 0.011533110469299761),
 ('tea', 0.011525701267122159),
 ('beatle', 0.011506085419961306),
 ('tate', 0.011506085419961306),
 ('shakespeare', 0.011506085419961306),
 ('europe', 0.011485480957296521),
 ('relic', 0.01148521064681669),
 ('hrs', 0.011467149004892154),
 ('outlet', 0.011402457919368452),
 ('tegenungan', 0.011400711356178175),
 ('gangga', 0.011400711356178175),
 ('aperitivo', 0.011295462216776587),
 ('destination', 0.01129450488621669),
 ('ghost', 0.011277303406638226),
 ('cocktail', 0.011264735503000873),
 ('tail', 0.011226756080159505),
 ('starlight', 0.011226756080159505),
 ('try', 0.011209941419126792),
 ('stanbul', 0.011198088491269407),
 ('kusadasi', 0.011198088491269407),
 ('cart', 0.011196460498895834),
 ('frascati', 0.011196460498895834),
 ('wild', 0.011158722572539935),
 ('picnic', 0.011120878510568059),
 ('palm', 0.011111510244773628),
 ('farmhouse', 0.011070636680554592),
 ('person', 0.011067676977329681),
 ('warner', 0.011067396954274344),
 ('dali', 0.011051691809306756),
 ('madrid', 0.011051691809306756),
 ('ripper', 0.011005820836484728),
 ('buckingham', 0.011005820836484728),
 ('cruiser', 0.011003058871761048),
 ('jetski', 0.011002634914606183),
 ('tibumana', 0.010993543093457528),
 ('taman', 0.010993543093457528),
 ('movie', 0.010989444272929982),
 ('venice', 0.010978047280266615),
 ('prince', 0.010953889278735038),
 ('beer', 0.010952011759077665),
 ('personal', 0.010936067063240667),
 ('idyllic', 0.010926852966196263),
 ('start', 0.010875113137143113),
 ('treatment', 0.010865427287371042),
 ('holy', 0.01079802426574954),
 ('padi', 0.010761472780501675),
 ('life', 0.010739721591496203),
 ('journey', 0.010729606002446518),
 ('minibus', 0.010692150893081103),
 ('wonderful', 0.0106697819783578),
 ('bros', 0.010637763918838746),
 ('istanbuls', 0.010608715412781545),
 ('immersive', 0.01060373247381574),
 ('easy', 0.010603340871580194),
 ('diver', 0.010597010503850118),
 ('reim', 0.010590865113212807),
 ('cabbelair', 0.010590865113212807),
 ('magical', 0.010582430611515906),
 ('change', 0.01058092148251886),
 ('dalí', 0.010561386949092635),
 ('global', 0.010557967846694867),
 ('lgw', 0.01050555625300815),
 ('stanste', 0.01050555625300815),
 ('emirate', 0.010500400005951024),
 ('escooter', 0.010463207200873275),
 ('restaurant', 0.010459422453866149),
 ('king', 0.010438223650278052),
 ('cefalu', 0.010398265708667238),
 ('saline', 0.010398265708667238),
 ('comiso', 0.010398265708667238),
 ('genealogy', 0.010398265708667238),
 ('ancestry', 0.010398265708667238),
 ('record', 0.010398265708667238),
 ('research', 0.010398265708667238),
 ('incl', 0.010397785275503281),
 ('cesarina', 0.010379389725602102),
 ('pisa', 0.01036709305453318),
 ...]

cv2 = CountVectorizer(analyzer='word', stop_words=stopwords_list, ngram_range=(2,2))
data3 = cv2.fit_transform(df_grouped['lemmatized'])
df_dtm3 = pd.DataFrame(data3.toarray(), columns=cv2.get_feature_names_out())
df_dtm3.index = df_grouped.index
df_dtm3

# Transposing document term matrix
df_dtm3 = df_dtm3.transpose()

# Plotting word cloud for each city
for index, city in enumerate(df_dtm3.columns):
    generate_wordcloud(df_dtm3[city].sort_values(ascending=False), city)

# Look at top bi-grams (in total, not per city)
sum_words = data3.sum(axis=0)
words_freq = [(word, sum_words[0, idx]) for word, idx in cv2.vocabulary_.items()]
words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)
words_freq

[('private tour', 2228),
 ('private transfer', 1569),
 ('day tour', 824),
 ('city tour', 818),
 ('tour private', 813),
 ('desert safari', 765),
 ('guide tour', 625),
 ('walk tour', 618),
 ('day trip', 589),
 ('small group', 573),
 ('tour rome', 562),
 ('airport transfer', 549),
 ('skip line', 513),
 ('half day', 465),
 ('abu dhabi', 426),
 ('tour london', 424),
 ('tour istanbul', 392),
 ('private day', 386),
 ('tour bali', 350),
 ('sistine chapel', 350),
 ('dubai city', 346),
 ('tour dubai', 344),
 ('vatican museum', 311),
 ('walking tour', 307),
 ('tour day', 304),
 ('food tour', 295),
 ('tour paris', 294),
 ('quad bike', 292),
 ('airport private', 291),
 ('day private', 290),
 ('private guide', 288),
 ('bbq dinner', 285),
 ('group tour', 268),
 ('tanah lot', 259),
 ('cooking class', 256),
 ('camel ride', 252),
 ('rome private', 250),
 ('transfer rome', 249),
 ('vice versa', 236),
 ('shore excursion', 230),
 ('roman forum', 230),
 ('london private', 223),
 ('wine taste', 207),
 ('sightseeing tour', 206),
 ('tour barcelona', 206),
 ('nusa penida', 201),
 ('rome city', 198),
 ('mount batur', 192),
 ('paris private', 190),
 ('arrival transfer', 188),
 ('heathrow airport', 185),
 ('museum sistine', 185),
 ('bike tour', 183),
 ('burj khalifa', 182),
 ('transfer private', 179),
 ('safari dubai', 178),
 ('rice terrace', 177),
 ('transfer paris', 177),
 ('transfer dubai', 174),
 ('red dune', 173),
 ('city center', 172),
 ('fullday tour', 168),
 ('departure transfer', 167),
 ('phi phi', 167),
 ('private walking', 164),
 ('cesarinas home', 163),
 ('bali tour', 160),
 ('dhabi city', 160),
 ('bali private', 159),
 ('transfer london', 157),
 ('private car', 155),
 ('street food', 155),
 ('dubai desert', 153),
 ('tour include', 151),
 ('barcelona private', 151),
 ('private airport', 149),
 ('tiramisu class', 148),
 ('private arrival', 146),
 ('dinner cruise', 145),
 ('local guide', 144),
 ('ancient rome', 143),
 ('safari bbq', 140),
 ('colosseum roman', 140),
 ('batur sunrise', 138),
 ('palatine hill', 138),
 ('dubai private', 137),
 ('tour lunch', 136),
 ('tour skip', 136),
 ('sagrada familia', 136),
 ('central london', 136),
 ('class cesarinas', 136),
 ('tour local', 135),
 ('tour guide', 134),
 ('white water', 133),
 ('ubud tour', 133),
 ('tour colosseum', 133),
 ('tour vatican', 132),
 ('fiumicino airport', 131),
 ('paris city', 130),
 ('home taste', 130),
 ('civitavecchia port', 129),
 ('sunrise trek', 126),
 ('private departure', 125),
 ('tour ubud', 125),
 ('london city', 125),
 ('transfer palermo', 124),
 ('cruise port', 121),
 ('tour good', 120),
 ('istanbul airport', 119),
 ('transfer barcelona', 114),
 ('louvre museum', 114),
 ('eiffel tower', 114),
 ('day istanbul', 113),
 ('rome day', 113),
 ('gate heaven', 112),
 ('morning desert', 112),
 ('forum palatine', 112),
 ('transfer airport', 111),
 ('istanbul day', 109),
 ('palermo airport', 109),
 ('sand boarding', 108),
 ('water raft', 107),
 ('car charter', 107),
 ('dhow cruise', 107),
 ('arrival private', 106),
 ('evening desert', 105),
 ('hotel pickup', 104),
 ('barcelona airport', 104),
 ('versa private', 104),
 ('london london', 103),
 ('james bond', 103),
 ('rome rome', 103),
 ('bali airport', 102),
 ('market tour', 102),
 ('dubai airport', 102),
 ('fullday private', 101),
 ('luxury van', 101),
 ('dune bash', 101),
 ('wine tour', 100),
 ('dubai dubai', 100),
 ('paris airport', 100),
 ('private dubai', 99),
 ('turkey tour', 99),
 ('transfer catania', 99),
 ('bali day', 98),
 ('speed boat', 98),
 ('temple tour', 97),
 ('departure private', 97),
 ('old city', 97),
 ('windsor castle', 97),
 ('paris paris', 97),
 ('entrance ticket', 96),
 ('cruise dinner', 96),
 ('pasta tiramisu', 96),
 ('dubai marina', 95),
 ('sunset tour', 93),
 ('round trip', 93),
 ('lot temple', 93),
 ('guide private', 93),
 ('tour abu', 93),
 ('admission ticket', 92),
 ('private fullday', 92),
 ('segway tour', 92),
 ('business car', 92),
 ('trip rome', 92),
 ('tour package', 91),
 ('london heathrow', 91),
 ('rome tour', 91),
 ('sitia jsh', 90),
 ('jsh airport', 90),
 ('dinner dubai', 90),
 ('river cruise', 90),
 ('hot spring', 89),
 ('island tour', 89),
 ('istanbul cappadocia', 89),
 ('monkey forest', 88),
 ('uluwatu temple', 88),
 ('tour hour', 88),
 ('day night', 88),
 ('night tour', 88),
 ('kid family', 88),
 ('st peters', 88),
 ('private driver', 86),
 ('istanbul private', 86),
 ('charles de', 84),
 ('jungle swing', 83),
 ('food wine', 83),
 ('heraklion airport', 83),
 ('tour phuket', 83),
 ('dune desert', 82),
 ('rome night', 82),
 ('airport rome', 82),
 ('rome colosseum', 81),
 ('blue lagoon', 80),
 ('instagram tour', 80),
 ('shopping tour', 80),
 ('barcelona city', 80),
 ('safari camel', 80),
 ('de gaulle', 80),
 ('airport cdg', 80),
 ('tour fullday', 79),
 ('hotel pick', 79),
 ('ephesus pamukkale', 79),
 ('free wifi', 78),
 ('istanbul tour', 78),
 ('chapel st', 78),
 ('atv ride', 77),
 ('semi private', 77),
 ('tour hotel', 77),
 ('tour kid', 77),
 ('dubai tour', 77),
 ('transfer fiumicino', 77),
 ('ayung river', 76),
 ('seine river', 76),
 ('de mallorca', 76),
 ('good rome', 76),
 ('bosphorus cruise', 75),
 ('cappadocia tour', 75),
 ('transfer heathrow', 75),
 ('ciampino airport', 75),
 ('halfday tour', 74),
 ('luxury car', 74),
 ('hour private', 74),
 ('boat tour', 74),
 ('ferrari world', 74),
 ('day cappadocia', 74),
 ('bali swing', 73),
 ('tour explore', 73),
 ('safari quad', 73),
 ('city airport', 72),
 ('palma de', 72),
 ('cook class', 71),
 ('crete private', 71),
 ('share transfer', 71),
 ('istanbul city', 71),
 ('trip paris', 71),
 ('smallgroup tour', 70),
 ('harry potter', 70),
 ('rome airport', 69),
 ('transfer hotel', 68),
 ('airport hotel', 68),
 ('waterfall tour', 68),
 ('line ticket', 68),
 ('private vehicle', 68),
 ('balloon ride', 68),
 ('old town', 68),
 ('private pizza', 68),
 ('catania cta', 68),
 ('cta airport', 68),
 ('hotel transfer', 67),
 ('local photographer', 67),
 ('private pasta', 67),
 ('pizza tiramisu', 67),
 ('east bali', 66),
 ('water park', 66),
 ('atv quad', 66),
 ('day dubai', 66),
 ('smallgroup street', 66),
 ('audio tour', 65),
 ('tour burj', 65),
 ('tower london', 65),
 ('phi island', 65),
 ('peters basilica', 65),
 ('jet ski', 64),
 ('highlight tour', 64),
 ('tour half', 64),
 ('lot sunset', 64),
 ('transfer istanbul', 64),
 ('gatwick airport', 64),
 ('rome vatican', 64),
 ('amalfi coast', 64),
 ('good ubud', 63),
 ('kintamani volcano', 63),
 ('local private', 63),
 ('istanbul istanbul', 63),
 ('mallorca airport', 63),
 ('phang nga', 63),
 ('transfer bali', 62),
 ('barcelona barcelona', 62),
 ('costa brava', 62),
 ('taste tour', 62),
 ('tour skiptheline', 62),
 ('roundtrip transfer', 61),
 ('creta private', 61),
 ('hagia sophia', 61),
 ('etna taormina', 61),
 ('private bali', 60),
 ('ticket private', 60),
 ('private shore', 60),
 ('fast track', 60),
 ('burj al', 60),
 ('colosseum tour', 60),
 ('hour tour', 59),
 ('private photo', 59),
 ('session local', 59),
 ('private london', 59),
 ('bali car', 58),
 ('airport pmi', 58),
 ('orly airport', 58),
 ('lempuyang temple', 57),
 ('experience private', 57),
 ('al arab', 57),
 ('airport dxb', 57),
 ('airport london', 57),
 ('airport fco', 57),
 ('hotel private', 56),
 ('transfer tofrom', 56),
 ('grand mosque', 56),
 ('bond island', 56),
 ('line colosseum', 56),
 ('bali bali', 55),
 ('transfer service', 55),
 ('scuba diving', 55),
 ('private half', 55),
 ('tour small', 55),
 ('hot air', 55),
 ('air balloon', 55),
 ('museum private', 55),
 ('ride bbq', 55),
 ('trip london', 55),
 ('vatican sistine', 55),
 ('vatican city', 55),
 ('batur volcano', 54),
 ('hopon hopoff', 54),
 ('skiptheline ticket', 54),
 ('gaulle airport', 54),
 ('phuket city', 54),
 ('include lunch', 53),
 ('dinner private', 53),
 ('tour visit', 53),
 ('transfer heraklion', 53),
 ('dubai abu', 53),
 ('nga bay', 53),
 ('catania airport', 53),
 ('ngurah rai', 52),
 ('tour transfer', 52),
 ('chania chq', 52),
 ('chq airport', 52),
 ('belly dance', 52),
 ('dune buggy', 52),
 ('sabiha gokcen', 52),
 ('luton airport', 52),
 ('colosseum ancient', 52),
 ('tour palermo', 52),
 ('palermo pmo', 52),
 ('pmo airport', 52),
 ('bali atv', 51),
 ('raft adventure', 51),
 ('telaga waja', 51),
 ('island day', 51),
 ('volcano tour', 51),
 ('private trip', 51),
 ('tour night', 51),
 ('live show', 51),
 ('lunch private', 50),
 ('penida island', 50),
 ('package day', 50),
 ('cruise dubai', 50),
 ('gokcen airport', 50),
 ('cappadocia ephesus', 50),
 ('london airport', 50),
 ('cdg paris', 50),
 ('rome walk', 50),
 ('nusa dua', 49),
 ('virtual tour', 49),
 ('bali ubud', 49),
 ('car private', 49),
 ('private luxury', 49),
 ('private cooking', 49),
 ('sheikh zaye', 49),
 ('vatican tour', 49),
 ('day bali', 48),
 ('driver private', 48),
 ('gothic quarter', 48),
 ('tour city', 48),
 ('street art', 48),
 ('transfer sitia', 48),
 ('istanbul old', 48),
 ('airport paris', 48),
 ('local home', 48),
 ('bali good', 47),
 ('port private', 47),
 ('private city', 47),
 ('tour vip', 47),
 ('dubai day', 47),
 ('dubai morning', 47),
 ('tour desert', 47),
 ('istanbul bosphorus', 47),
 ('phuket airport', 47),
 ('transfer civitavecchia', 47),
 ('tour catania', 47),
 ('tour taormina', 47),
 ('good bali', 46),
 ('airport arrival', 46),
 ('cycling tour', 46),
 ('car tour', 46),
 ('heritage tour', 46),
 ('photo session', 46),
 ('tour tour', 46),
 ('night private', 46),
 ('tour professional', 46),
 ('blue mosque', 46),
 ('tour louvre', 46),
 ('topkapi palace', 46),
 ('london tour', 46),
 ('rome civitavecchia', 46),
 ('tour etna', 46),
 ('uluwatu sunset', 45),
 ('river raft', 45),
 ('cruise terminal', 45),
 ('tour ferrari', 45),
 ('transfer chania', 45),
 ('dhabi tour', 45),
 ('private istanbul', 45),
 ('villa deste', 45),
 ('temple bali', 44),
 ('ubud bali', 44),
 ('ubud private', 44),
 ('international airport', 44),
 ('museum tour', 44),
 ('experience barcelona', 44),
 ('ticket transfer', 44),
 ('dinner live', 44),
 ('dubai frame', 44),
 ('phuket phuket', 44),
 ('port rome', 44),
 ('palermo private', 44),
 ('tukad cepung', 43),
 ('natural hot', 43),
 ('halfday private', 43),
 ('jatiluwih rice', 43),
 ('adventure bali', 43),
 ('tour secret', 43),
 ('photo shoot', 43),
 ('ebike tour', 43),
 ('city centre', 43),
 ('airport central', 43),
 ('trevi fountain', 43),
 ('north bali', 42),
 ('adventure tour', 42),
 ('tour uluwatu', 42),
 ('waterfall bali', 42),
 ('water sport', 42),
 ('fullday bali', 42),
 ('city private', 42),
 ('southampton cruise', 42),
 ('transfer phuket', 42),
 ('rome hotel', 42),
 ('bali instagram', 41),
 ('mt batur', 41),
 ('ubud day', 41),
 ('tour tanah', 41),
 ('photo tour', 41),
 ('city sightseeing', 41),
 ('tour wine', 41),
 ('transfer central', 41),
 ('dubai evening', 41),
 ('loire valley', 41),
 ('private rome', 41),
 ('bali quad', 40),
 ('tour nusa', 40),
 ('photography tour', 40),
 ('highlight private', 40),
 ('trip bali', 40),
 ('lunch include', 40),
 ('lunch dinner', 40),
 ('taxi tour', 40),
 ('bus tour', 40),
 ('boat trip', 40),
 ('dining experience', 40),
 ('latin quarter', 40),
 ('british museum', 40),
 ('day rome', 40),
 ('package bali', 39),
 ('tour mount', 39),
 ('make class', 39),
 ('private group', 39),
 ('taste private', 39),
 ('tour ancient', 39),
 ('dune safari', 39),
 ('westminster abbey', 39),
 ('paris orly', 39),
 ('st peter', 39),
 ('colosseum forum', 39),
 ('private vatican', 39),
 ('colosseum vatican', 39),
 ('mount etna', 39),
 ('trip transfer', 38),
 ('airport dps', 38),
 ('buffet dinner', 38),
 ('tour discover', 38),
 ('line private', 38),
 ('private minivan', 38),
 ('ticket dubai', 38),
 ('ride dubai', 38),
 ('cappadocia pamukkale', 38),
 ('london day', 38),
 ('coral island', 38),
 ('valley temple', 38),
 ('water temple', 37),
 ('expert guide', 37),
 ('family tour', 37),
 ('miracle garden', 37),
 ('khalifa ticket', 37),
 ('ride option', 37),
 ('london walk', 37),
 ('tofrom london', 37),
 ('paris charles', 37),
 ('speedboat phuket', 37),
 ('chapel tour', 37),
 ('civitavecchia cruise', 37),
 ('port civitavecchia', 37),
 ('kecak dance', 36),
 ('beach tour', 36),
 ('english speaking', 36),
 ('early morning', 36),
 ('atv bike', 36),
 ('local expert', 36),
 ('trip barcelona', 36),
 ('horseback ride', 36),
 ('city luxury', 36),
 ('entry ticket', 36),
 ('airport creta', 36),
 ('ride sand', 36),
 ('layover tour', 36),
 ('day turkey', 36),
 ('tour turkey', 36),
 ('jewish ghetto', 36),
 ('rome fiumicino', 36),
 ('rome highlight', 36),
 ('transfer ciampino', 36),
 ('rome florence', 36),
 ('taormina private', 36),
 ('bike adventure', 35),
 ('lunch bali', 35),
 ('temple private', 35),
 ('palace private', 35),
 ('electric bike', 35),
 ('kintamani tour', 35),
 ('tour royal', 35),
 ('private sightseeing', 35),
 ('tour luxury', 35),
 ('luxury private', 35),
 ('group day', 35),
 ('private barcelona', 35),
 ('city highlight', 35),
 ('treasure hunt', 35),
 ('city heraklion', 35),
 ('dhabi dubai', 35),
 ('guide walk', 35),
 ('pamukkale tour', 35),
 ('london postcode', 35),
 ('khai island', 35),
 ('skiptheline colosseum', 35),
 ('line vatican', 35),
 ('penida tour', 34),
 ('temple sunset', 34),
 ('cultural tour', 34),
 ('night day', 34),
 ('private vacation', 34),
 ('luxury vehicle', 34),
 ('private walk', 34),
 ('safari tour', 34),
 ('dubai red', 34),
 ('private abu', 34),
 ('london gatwick', 34),
 ('castle private', 34),
 ('hotel rome', 34),
 ('excursion civitavecchia', 34),
 ('bali fullday', 33),
 ('tour highlight', 33),
 ('ubud village', 33),
 ('village tour', 33),
 ('ubud kintamani', 33),
 ('speaking driver', 33),
 ('pick drop', 33),
 ('class lunch', 33),
 ('tour exclusive', 33),
 ('cruise private', 33),
 ('boarding camel', 33),
 ('wild wadi', 33),
 ('safari dune', 33),
 ('trip istanbul', 33),
 ('pamukkale ephesus', 33),
 ('london southampton', 33),
 ('airport lhr', 33),
 ('hampton court', 33),
 ('paris tour', 33),
 ('group market', 33),
 ('taormina castelmola', 33),
 ('jeep tour', 32),
 ('fire dance', 32),
 ('evening tour', 32),
 ('water rafting', 32),
 ('ulun danu', 32),
 ('tour amazing', 32),
 ('audio guide', 32),
 ('vip private', 32),
 ('dubai creek', 32),
 ('global village', 32),
 ('dubai hotel', 32),
 ('black cab', 32),
 ('tour speed', 32),
 ('appian way', 32),
 ('rome center', 32),
 ('tour experience', 31),
 ('buffet lunch', 31),
 ('visit private', 31),
 ('tour free', 31),
 ('guide day', 31),
 ('way private', 31),
 ('photography session', 31),
 ('food taste', 31),
 ('tour roman', 31),
 ('museum guide', 31),
 ('olive oil', 31),
 ('knossos palace', 31),
 ('tour chania', 31),
 ('dinner belly', 31),
 ('dubai night', 31),
 ('dhow dinner', 31),
 ('istanbul ephesus', 31),
 ('airport ist', 31),
 ('city london', 31),
 ('london windsor', 31),
 ('london luton', 31),
 ('hill roman', 31),
 ('arena floor', 31),
 ('tour blue', 30),
 ('afternoon tour', 30),
 ('ubud waterfall', 30),
 ('day car', 30),
 ('tour sunset', 30),
 ('trip private', 30),
 ('private ubud', 30),
 ('tour east', 30),
 ('private food', 30),
 ('morning tour', 30),
 ('minute private', 30),
 ('private chauffeur', 30),
 ('city dubai', 30),
 ('ephesus tour', 30),
 ('tour semiprivate', 30),
 ('fast access', 30),
 ('disneyland paris', 30),
 ('colosseum underground', 30),
 ('colosseum arena', 30),
 ('tour sistine', 30),
 ('lagoon snorkeling', 29),
 ('self drive', 29),
 ('coffee plantation', 29),
 ('tour driver', 29),
 ('private round', 29),
 ('explore tour', 29),
 ('include private', 29),
 ('like local', 29),
 ('safari private', 29),
 ('vacation photography', 29),
 ('tour sagrada', 29),
 ('airport city', 29),
 ('transfer luxury', 29),
 ('private personalize', 29),
 ('modern dubai', 29),
 ('safe private', 29),
 ('day abu', 29),
 ('tour bosphorus', 29),
 ('stonehenge bath', 29),
 ('private paris', 29),
 ('paris night', 29),
 ('simon cabaret', 29),
 ('rome skip', 29),
 ('chapel vatican', 29),
 ('borghese gallery', 29),
 ('fullday rome', 29),
 ('open water', 28),
 ('kecak fire', 28),
 ('river rafting', 28),
 ('ticket include', 28),
 ('private evening', 28),
 ('arrival departure', 28),
 ('bike ride', 28),
 ('tour smallgroup', 28),
 ('ticket day', 28),
 ('tour speedboat', 28),
 ('ride private', 28),
 ('private sailing', 28),
 ('combo tour', 28),
 ('tour cooking', 28),
 ('wine tasting', 28),
 ('tour old', 28),
 ('vehicle private', 28),
 ('roundtrip private', 28),
 ('experience dubai', 28),
 ('zaye grand', 28),
 ('private basis', 28),
 ('new airport', 28),
 ('grand bazaar', 28),
 ('st pauls', 28),
 ('court palace', 28),
 ('paris day', 28),
 ('taste rome', 28),
 ('giardini naxos', 28),
 ('raft bali', 27),
 ('private halfday', 27),
 ('sunset dinner', 27),
 ('trip lunch', 27),
 ('rice field', 27),
 ('lot tour', 27),
 ('bali mount', 27),
 ('tour pickup', 27),
 ('tour walk', 27),
 ('hotel airport', 27),
 ('culture tour', 27),
 ('market visit', 27),
 ('tour optional', 27),
 ('de la', 27),
 ('day guide', 27),
 ('transfer city', 27),
 ('barcelona tour', 27),
 ('tour option', 27),
 ('area customer', 27),
 ('khalifa floor', 27),
 ('dinner camel', 27),
 ('zaye mosque', 27),
 ('tour st', 27),
 ('luxury transfer', 27),
 ('orsay museum', 27),
 ('cdg airport', 27),
 ('versaille palace', 27),
 ('big boat', 27),
 ('excursion rome', 27),
 ('chapel private', 27),
 ('tour messina', 27),
 ('sekumpul waterfall', 26),
 ('experience bali', 26),
 ('besakih temple', 26),
 ('balinese cooking', 26),
 ('cepung waterfall', 26),
 ('tirta gangga', 26),
 ('horse ride', 26),
 ('bali waterfall', 26),
 ('tour english', 26),
 ('luxury yacht', 26),
 ('group private', 26),
 ('walk private', 26),
 ('tour morning', 26),
 ('barcelona highlight', 26),
 ('airport bcn', 26),
 ('private wine', 26),
 ('city business', 26),
 ('city chania', 26),
 ('smallgroup day', 26),
 ('bosphorus dinner', 26),
 ('cappadocia day', 26),
 ('airport istanbul', 26),
 ('london eye', 26),
 ('pauls cathedral', 26),
 ('black taxi', 26),
 ('royal london', 26),
 ('city paris', 26),
 ('sea canoe', 26),
 ('golf cart', 26),
 ('rome small', 26),
 ('vatican museums', 26),
 ('tour civitavecchia', 26),
 ('group pasta', 26),
 ('colosseum guide', 26),
 ('colosseum private', 26),
 ('palermo monreale', 26),
 ('snorkeling tour', 25),
 ('private allinclusive', 25),
 ('tour halfday', 25),
 ('raft ubud', 25),
 ('tour enjoy', 25),
 ('private family', 25),
 ('beach private', 25),
 ('tirta empul', 25),
 ('hide gem', 25),
 ('private custom', 25),
 ('pickup private', 25),
 ('tour fast', 25),
 ('island private', 25),
 ('transfer fromto', 25),
 ('island speedboat', 25),
 ('history tour', 25),
 ('guide walking', 25),
 ('escape game', 25),
 ('samaria gorge', 25),
 ('sandboarde camel', 25),
 ('world adventure', 25),
 ('cruise tour', 25),
 ('marina dhow', 25),
 ('warner bros', 25),
 ('tour cappadocia', 25),
 ('ephesus cappadocia', 25),
 ('airport see', 25),
 ('churchill war', 25),
 ('war room', 25),
 ('tour palma', 25),
 ('transfer palma', 25),
 ('moulin rouge', 25),
 ('airport ory', 25),
 ('boat phuket', 25),
 ('vatican colosseum', 25),
 ('rome local', 25),
 ('civitavecchia rome', 25),
 ('ubud jungle', 24),
 ('waja river', 24),
 ('service private', 24),
 ('tour car', 24),
 ('tour kintamani', 24),
 ('ubud tanah', 24),
 ('art tour', 24),
 ('driver bali', 24),
 ('art village', 24),
 ('spa package', 24),
 ('professional guide', 24),
 ('island snorkeling', 24),
 ('seafood dinner', 24),
 ('private excursion', 24),
 ('airport departure', 24),
 ('virtual reality', 24),
 ('local market', 24),
 ('day excursion', 24),
 ('professional photographer', 24),
 ('park güell', 24),
 ('secret food', 24),
 ('train station', 24),
 ('center private', 24),
 ('tour family', 24),
 ('independent day', 24),
 ('dubai burj', 24),
 ('img world', 24),
 ('bash camel', 24),
 ('safari sand', 24),
 ('private vip', 24),
 ('pizza make', 24),
 ('vip tour', 24),
 ('good istanbul', 24),
 ('cruise istanbul', 24),
 ('fromto istanbul', 24),
 ('dolmabahce palace', 24),
 ('center hotel', 24),
 ('castle stonehenge', 24),
 ('london hotel', 24),
 ('day london', 24),
 ('phuket private', 24),
 ('class rome', 24),
 ('private colosseum', 24),
 ('share pasta', 24),
 ('pasta love', 24),
 ('love small', 24),
 ('etna tour', 24),
 ('temple waterfall', 23),
 ('heaven bali', 23),
 ('trek natural', 23),
 ('swing ubud', 23),
 ('tour gate', 23),
 ('waterfall private', 23),
 ('sunrise trekking', 23),
 ('tour lempuyang', 23),
 ('tour airport', 23),
 ('day day', 23),
 ('private shuttle', 23),
 ('tour evening', 23),
 ('pickup dropoff', 23),
 ('tour people', 23),
 ('premium tour', 23),
 ('hill tour', 23),
 ('excursion private', 23),
 ('tour tower', 23),
 ('hopoff bus', 23),
 ('airport barcelona', 23),
 ('electric scooter', 23),
 ('day city', 23),
 ('private roundtrip', 23),
 ('private hour', 23),
 ('group guide', 23),
 ('chania airport', 23),
 ('private yacht', 23),
 ('lose chamber', 23),
 ('desert dune', 23),
 ('private desert', 23),
 ('camel riding', 23),
 ('private way', 23),
 ('north goa', 23),
 ('istanbul new', 23),
 ('change guard', 23),
 ('tour workshop', 23),
 ('paris louvre', 23),
 ('day paris', 23),
 ('musée dorsay', 23),
 ('rome food', 23),
 ('peter basilica', 23),
 ('tour pompeii', 23),
 ('san gimignano', 23),
 ('airport palermo', 23),
 ('bali snorkeling', 22),
 ('tour jungle', 22),
 ('tour dinner', 22),
 ('tour inclusive', 22),
 ('explore ubud', 22),
 ('class private', 22),
 ('lot uluwatu', 22),
 ('tour big', 22),
 ('bali sunrise', 22),
 ('discovery tour', 22),
 ('park ticket', 22),
 ('river tubing', 22),
 ('package tour', 22),
 ('snorkel tour', 22),
 ('park private', 22),
 ('luxury tour', 22),
 ('tour goa', 22),
 ('priority access', 22),
 ('pickup barcelona', 22),
 ('group walk', 22),
 ('wine cheese', 22),
 ('park guell', 22),
 ('hop hop', 22),
 ('barcelona local', 22),
 ('city walk', 22),
 ('line tour', 22),
 ('food market', 22),
 ('yacht cruise', 22),
 ('safari red', 22),
 ('dubai sightseeing', 22),
 ('safari dinner', 22),
 ('park dubai', 22),
 ('lunch dubai', 22),
 ('way transfer', 22),
 ('wadi water', 22),
 ('guide istanbul', 22),
 ('line guide', 22),
 ('day ephesus', 22),
 ('turkey day', 22),
 ('semiprivate max', 22),
 ('airport lcy', 22),
 ('semiprivate tour', 22),
 ('paris eiffel', 22),
 ('tour montmartre', 22),
 ('paris walk', 22),
 ('phuket phi', 22),
 ('fly hanuman', 22),
 ('civita di', 22),
 ('airport cia', 22),
 ('piazza armerina', 22),
 ('ubud monkey', 21),
 ('private fishing', 21),
 ('traditional village', 21),
 ('lunch day', 21),
 ('mother temple', 21),
 ('cooking experience', 21),
 ('good waterfall', 21),
 ('car driver', 21),
 ('denpasar city', 21),
 ('rai airport', 21),
 ('ubud art', 21),
 ('beach day', 21),
 ('vacation photographer', 21),
 ('private boat', 21),
 ('van private', 21),
 ('afternoon tea', 21),
 ('sailing experience', 21),
 ('barcelona montserrat', 21),
 ('city barcelona', 21),
 ('quarter private', 21),
 ('cable car', 21),
 ('barcelona day', 21),
 ('lunch barcelona', 21),
 ('experience local', 21),
 ('skiptheline private', 21),
 ('tour taste', 21),
 ('private morning', 21),
 ('food experience', 21),
 ('palace tour', 21),
 ('ski dubai', 21),
 ('transfer abu', 21),
 ('dubai dinner', 21),
 ('theme park', 21),
 ('prince island', 21),
 ('istanbul include', 21),
 ('istanbul sabiha', 21),
 ('max people', 21),
 ('jack ripper', 21),
 ('buckingham palace', 21),
 ('port london', 21),
 ('tour windsor', 21),
 ('paris versaille', 21),
 ('notre dame', 21),
 ('trip phuket', 21),
 ('rome guide', 21),
 ('colosseum express', 21),
 ('rome pompeii', 21),
 ('airport civitavecchia', 21),
 ('etna wine', 21),
 ('car hire', 20),
 ('bali water', 20),
 ('package private', 20),
 ('heaven gate', 20),
 ...]

df.head()

# Preview what I want to drop
df.loc[df['Attraction'].str.contains('airport')]

# Get rid of the airport transfer 'attractions'
df2 = df.drop(df.loc[df['Attraction'].str.contains('airport')].index)

df2 = df.drop(df2.loc[df2['Attraction'].str.contains('transfer')].index)

# Just in case, add these words to the stopwords list
stopwords_list += ['airport', 'transfer', 'private']

print(df.shape)
print(df2.shape)

(27533, 4)
(25315, 4)

def preprocess_df(df, column, preview=True, lemmatize=True):
    """
    Input df with raw text attractions.
    Return df with preprocessed text.
    If preview=True, returns a preview of the new df.
    """
    
    df[column] = df['Attraction'].apply(lambda x: x.lower())
    df[column] = df[column].apply(lambda x: re.sub('[%s]' % re.escape(string.punctuation), '', x))
    df[column] = df[column].apply(lambda x: re.sub('\w*\d\w*','', x))
    
    if lemmatize:
        df[column] = df[column].apply(lambda x: ' '.join(
                                        [token.lemma_ for token in list(nlp(x)) if (token.is_stop==False)]))
    if preview:
        display(df.head(10))
        
    return df

def group_text_per_city(df, column):
    """
    Groups the preprocessed text per city.
    """
    df_to_group = df[['City', column]]
    df_grouped = df_to_group.groupby(by='City').agg(lambda x:' '.join(x))
    return df_grouped

def create_doc_term_matrix(df, column, count_vec=True, ngram_range=(1,1)):
    """
    Creates a document term matrix.
    Defaults to count vectorizer with optional n-gram param.
    If count_vec==False, uses a TF-IDF vectorizer.
    """
    df_grouped = group_text_per_city(df, column)
    
    if count_vec:
        vec = CountVectorizer(analyzer='word', stop_words=stopwords_list, ngram_range=ngram_range)
    else:
        vec = TfidfVectorizer(analyzer='word', stop_words=stopwords_list)
    
    data = vec.fit_transform(df_grouped[column])
    df_dtm = pd.DataFrame(data.toarray(), columns=vec.get_feature_names_out())
    df_dtm.index = df_grouped.index
    return df_dtm.transpose()

preprocessed_df = preprocess_df(df2, 'lemmatized')
dtm_cv = create_doc_term_matrix(preprocessed_df, 'lemmatized', count_vec=True)

for index, city in enumerate(dtm_cv.columns):
    generate_wordcloud(dtm_cv[city].sort_values(ascending=False), city)

dtm_tfidf = create_doc_term_matrix(df2, 'lemmatized', count_vec=False)

for index, city in enumerate(dtm_tfidf.columns):
    generate_wordcloud(dtm_tfidf[city].sort_values(ascending=False), city)

dtm_bigram = create_doc_term_matrix(df2, 'lemmatized',
                                    count_vec=True, ngram_range=(2,2))

for index, city in enumerate(dtm_bigram.columns):
    generate_wordcloud(dtm_bigram[city].sort_values(ascending=False), city)

def generate_better_wordcloud(data, title, mask=None):
    cloud = WordCloud(scale=3, max_words=150, colormap='tab20c', mask=mask,
                      background_color='white').generate_from_frequencies(data)
    plt.figure(figsize=(10,8))
    plt.imshow(cloud, interpolation='bilinear')
    plt.axis('off')
    plt.title('\n'.join(wrap(title,60)), fontsize=13)
    plt.show()

mask = np.array(Image.open('../Images/palm2.jpg'))

generate_better_wordcloud(dtm_tfidf['Bali, Indonesia'].sort_values(ascending=False),
                          'Bali, Indonesia', mask=mask)

mask_london = np.array(Image.open('../Images/ben.jpg'))

generate_better_wordcloud(dtm_tfidf['London, United Kingdom'].sort_values(ascending=False),
                          'London, United Kingdom', mask=mask_london)

mask_paris = np.array(Image.open('../Images/paris.jpg'))
generate_better_wordcloud(dtm_tfidf['Paris, France'].sort_values(ascending=False),
                          'Paris, France', mask=mask_paris)

mask_rome = np.array(Image.open('../Images/italy.jpg'))
generate_better_wordcloud(dtm_tfidf['Rome, Italy'].sort_values(ascending=False),
                          'Rome, Italy', mask=mask_rome)

mask_sicily = np.array(Image.open('../Images/sicily.jpg'))
generate_better_wordcloud(dtm_tfidf['Sicily, Italy'].sort_values(ascending=False),
                          'Sicily, Italy', mask=mask_sicily)

mask_barca = np.array(Image.open('../Images/spain_flag.jpg'))
generate_better_wordcloud(dtm_tfidf['Barcelona, Spain'].sort_values(ascending=False),
                          'Barcelona, Spain', mask=mask_barca)

mask_dubai = np.array(Image.open('../Images/dubai.jpg'))
generate_better_wordcloud(dtm_tfidf['Dubai, United Arab Emirates'].sort_values(ascending=False),
                          'Dubai, United Arab Emirates', mask=mask_dubai)

generate_better_wordcloud(dtm_tfidf['Goa, India'].sort_values(ascending=False),
                          'Goa, India', mask=mask)

mask_istanbul = np.array(Image.open('../Images/hagia_sophia.jpg'))
generate_better_wordcloud(dtm_tfidf['Istanbul, Turkey'].sort_values(ascending=False),
                          'Istanbul, Turkey', mask=mask_istanbul)

generate_better_wordcloud(dtm_tfidf['Phuket, Thailand'].sort_values(ascending=False),
                          'Phuket, Thailand', mask=mask)

generate_better_wordcloud(dtm_tfidf['Majorca, Balearic Islands'].sort_values(ascending=False),
                          'Majorca, Balearic Islands', mask=mask)

generate_better_wordcloud(dtm_tfidf['Crete, Greece'].sort_values(ascending=False),
                          'Crete, Greece', mask=mask)

# Group the corpora by city and join them
df_to_group = preprocessed_df[['City', 'lemmatized']]
df_grouped = df_to_group.groupby(by='City').agg(lambda x:' '.join(x))
df_grouped

bali_text = df_grouped.loc['Bali, Indonesia', 'lemmatized']
fd = FreqDist(word_tokenize(bali_text))
fd.most_common(20)

[('bali', 2206),
 ('tour', 2190),
 ('private', 1025),
 ('ubud', 869),
 ('day', 669),
 ('temple', 512),
 ('waterfall', 402),
 ('batur', 292),
 ('transfer', 276),
 ('good', 273),
 ('water', 262),
 ('raft', 259),
 ('adventure', 254),
 ('airport', 253),
 ('nusa', 252),
 ('lot', 251),
 ('tanah', 242),
 ('fullday', 231),
 ('package', 226),
 ('sunrise', 225)]

city_freqs = {}
for city in df_grouped.index:
    city_text = df_grouped.loc[city, 'lemmatized']
    fd = FreqDist(word_tokenize(city_text))
    city_freqs[city] = fd.most_common(20)
city_freqs_df = pd.DataFrame(city_freqs)
city_freqs_df.head()

# Make one graph of most frequent words
yaxis = [x[0] for x in city_freqs_df['Bali, Indonesia']]
xaxis = [x[1] for x in city_freqs_df['Bali, Indonesia']]

plt.figure(figsize=(10,8))
sns.barplot(xaxis)
plt.title('Most Frequent Words: Bali, Indonesia')
plt.xlabel('Frequency')
plt.ylabel('Word')
plt.show()

# Make graphs for each city
for city in city_freqs_df.columns:    
    yaxis = [x[0] for x in city_freqs_df[city]]
    xaxis = [x[1] for x in city_freqs_df[city]]

    plt.figure(figsize=(10,8))
    sns.barplot(xaxis)
    plt.title(f'Most Frequent Words: {city}')
    plt.xlabel('Frequency')
    plt.ylabel('Word')
    plt.show()

# Re-import the data to get a fresh start
data = pd.read_csv('../Data/cities_df', index_col=0)
data.head()

# Perform train/test split before cleaning/preprocessing
X = data['Attraction']
y = data['City']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=123)
X_train.shape, X_test.shape

# Since this is a series, I will need to make it a DF for my preprocessing function
X_train

X_train_preprocessed = preprocess_df(pd.DataFrame(X_train, columns=['Attraction']), 'lemmatized')
X_test_preprocessed = preprocess_df(pd.DataFrame(X_test, columns=['Attraction']), 'lemmatized')

stopwords_list = stopwords.words('english')
stopwords_list += list(string.punctuation)
stopwords_list += ['airport', 'transfer', 'private']

# Vectorize the text data to be suitable for modeling
vectorizer = TfidfVectorizer(analyzer='word', stop_words=stopwords_list, decode_error='ignore')
X_train_tfidf = vectorizer.fit_transform(X_train_preprocessed['lemmatized'])
X_test_tfidf = vectorizer.transform(X_test_preprocessed['lemmatized'])

def plot_conf_matrix(y_true, y_pred):
    
    """
    Plots a confusion matrix and displays classification report.
    """
    
    cm = confusion_matrix(y_true, y_pred, normalize='true')
    plt.figure(figsize=(15, 15))
    sns.heatmap(cm, annot=True, cmap='Blues', fmt='0.2g', annot_kws={"size": 14},
                xticklabels=nb.classes_, yticklabels=nb.classes_, square=True)
    plt.xlabel('Predictions')
    plt.ylabel('Actuals')
    plt.show()

def evaluate_model(model, X_train, X_test):
    y_preds_train = model.predict(X_train.todense())
    y_preds_test = model.predict(X_test.todense())

    print('Training Accuracy:', accuracy_score(y_train, y_preds_train))
    print('Testing Accuracy:', accuracy_score(y_test, y_preds_test))
    print('\n---------------\n')
    print('Training F1:', f1_score(y_train, y_preds_train, average='weighted'))
    print('Testing F1:', f1_score(y_test, y_preds_test, average='weighted'))
    print('\n---------------\n')
    print('Train Confusion Matrix\n')
    plot_conf_matrix(y_train, y_preds_train)
    print('Test Confusion Matrix\n')
    plot_conf_matrix(y_test, y_preds_test)
    print('\n----------------\n')
    print(classification_report(y_test, y_preds_test))

nb = MultinomialNB()
nb.fit(X_train_tfidf.todense(), y_train)

nb.classes_

evaluate_model(nb, X_train_tfidf, X_test_tfidf)

# Compute class weights
class_weights = class_weight.compute_class_weight('balanced',
                                                  np.unique(y_train),
                                                  y_train)
weights_dict = dict(zip(np.unique(y_train), class_weights))
weights_dict

# Use class weights dictionary to calculate sample weight (needed for MultinomialNB)
sample_weights = class_weight.compute_sample_weight(weights_dict, y_train)

nb = MultinomialNB()
nb.fit(X_train_tfidf.todense(),
       y_train,
       sample_weight=sample_weights)

evaluate_model(nb, X_train_tfidf, X_test_tfidf)

new_stopwords = stopwords_list + ['bali', 'barcelona', 'crete', 'dubai',
                                  'istanbul', 'london', 'majorca', 'phuket',
                                  'paris', 'rome', 'sicily', 'mallorca', 'goa']

vectorizer = TfidfVectorizer(analyzer='word',
                             stop_words=new_stopwords,
                             decode_error='ignore')
X_train_tfidf = vectorizer.fit_transform(X_train_preprocessed['lemmatized'])
X_test_tfidf = vectorizer.transform(X_test_preprocessed['lemmatized'])

nb = MultinomialNB()
nb.fit(X_train_tfidf.todense(),
       y_train,
       sample_weight=sample_weights)

evaluate_model(nb, X_train_tfidf, X_test_tfidf)

# Save the Naive Bayes Model
joblib.dump(nb, '../nb_model')

# Continuing each new iteration without city names 
cv = CountVectorizer(analyzer='word',
                     stop_words=new_stopwords,
                     decode_error='ignore')
X_train_cv = cv.fit_transform(X_train_preprocessed['lemmatized'])
X_test_cv = cv.transform(X_test_preprocessed['lemmatized'])
nb_cv = MultinomialNB()
nb_cv.fit(X_train_cv.todense(),
          y_train,
          sample_weight=sample_weights)
evaluate_model(nb_cv, X_train_cv, X_test_cv)

bigram = CountVectorizer(analyzer='word',
                         stop_words=new_stopwords,
                         decode_error='ignore',
                         ngram_range=(2,2))
X_train_bg = bigram.fit_transform(X_train_preprocessed['lemmatized'])
X_test_bg = bigram.transform(X_test_preprocessed['lemmatized'])
nb_bg = MultinomialNB()
nb_bg.fit(X_train_bg.todense(),
          y_train,
          sample_weight=sample_weights)
evaluate_model(nb_bg, X_train_bg, X_test_bg)

vectorizer = TfidfVectorizer(analyzer='word',
                             stop_words=new_stopwords,
                             decode_error='ignore')
X_train_tfidf = vectorizer.fit_transform(X_train_preprocessed['lemmatized'])
X_test_tfidf = vectorizer.transform(X_test_preprocessed['lemmatized'])

rf = RandomForestClassifier(class_weight=weights_dict)
rf.fit(X_train_tfidf.todense(), y_train)

evaluate_model(rf, X_train_tfidf, X_test_tfidf)

#Get feature importances
feat_imps = pd.Series(rf.feature_importances_,
                      index=vectorizer.get_feature_names_out())
feat_imps[:11]

top_20_feats = feat_imps.sort_values(ascending=False).head(20)
top_20_feats

plt.figure(figsize=(10,8))
sns.barplot(x=top_20_feats, y=top_20_feats.index)
plt.title('Top 20 Features')
plt.ylabel('Word')
plt.xlabel('Importance')
plt.show()

X_train_cleaned = preprocess_df(pd.DataFrame(X_train, columns=['Attraction']),
                                'cleaned', lemmatize=False)
X_test_cleaned = preprocess_df(pd.DataFrame(X_test, columns=['Attraction']),
                               'cleaned', lemmatize=False)

vectorizer = TfidfVectorizer(analyzer='word',
                             stop_words=new_stopwords,
                             decode_error='ignore')
X_train_tfidf = vectorizer.fit_transform(X_train_cleaned['cleaned'])
X_test_tfidf = vectorizer.transform(X_test_cleaned['cleaned'])

nb_cleaned = MultinomialNB()
nb_cleaned.fit(X_train_tfidf.todense(),
       y_train,
       sample_weight=sample_weights)

evaluate_model(nb_cleaned, X_train_tfidf, X_test_tfidf)

# Save the best Naive Bayes Model
joblib.dump(nb_cleaned, '../non_lemmatized_model')

def preprocess_text(text):
    """
    Input raw text.
    Return preprocessed text.
    """
    
    preprocessed = text.lower()
    preprocessed = re.sub('[%s]' % re.escape(string.punctuation), '', preprocessed)
    preprocessed = re.sub('\w*\d\w*','', preprocessed)
        
    return [preprocessed]

raw_text = 'I want to go to the beach, go hiking and snorkeling'
preprocessed_text = preprocess_text(raw_text)
preprocessed_text

nb_cleaned.predict(vectorizer.transform(preprocessed_text))

preprocessed2 = preprocess_text('Go to historic museums')
print(preprocessed2)
nb_cleaned.predict(vectorizer.transform(preprocessed2))

preprocessed3 = preprocess_text('Wine tastings, long walks and dinners')
print(preprocessed3)
nb_cleaned.predict(vectorizer.transform(preprocessed3))

preprocessed4 = preprocess_text('Do yoga on the beach')
print(preprocessed4)
nb_cleaned.predict(vectorizer.transform(preprocessed4))

preprocessed5 = preprocess_text('Sunset cruises on a yacht with wine')
print(preprocessed5)
nb_cleaned.predict(vectorizer.transform(preprocessed5))

# Use OOP to get preprocessing steps into a pipeline
class PreprocessText(TransformerMixin):
    
    def __init__(self):
        self = self
    
    def fit(self, X, y=None, **fit_params):
        return self
        
    def transform(self, X, **transform_params):
        try:
            X = pd.DataFrame(X, columns=['Attraction'])
            X['cleaned'] = X['Attraction'].apply(lambda x: x.lower())
            X['cleaned'] = X['cleaned'].apply(lambda x: re.sub('[%s]' % re.escape(string.punctuation), '', x))
            X['cleaned'] = X['cleaned'].apply(lambda x: re.sub('\w*\d\w*','', x))
            
            X = X['cleaned']
        except:
            pass
        return X
    
class DenseTransformer():

    def __init__(self):
        self = self
    
    def fit(self, X, y=None, **fit_params):
        return self

    def transform(self, X, y=None, **fit_params):
        return X.todense()

# Test preprocessing class
prep = PreprocessText()
prep.transform(X_train)

pipe = Pipeline(steps=[
                ('TextPreprocessor', PreprocessText()),
                ('TFIDFVectorizer', TfidfVectorizer(analyzer='word',
                                                    stop_words=new_stopwords,
                                                    decode_error='ignore')),
                ('DenseTransformer', DenseTransformer()),
                ('NaiveBayes', MultinomialNB())])

set_config(display='diagram')

pipe.fit(X_train,
         y_train, 
         **{'NaiveBayes__sample_weight': sample_weights})

pipe.score(X_test, y_test)

pipe.predict(['I want to go snorkeling and tan on the beach'])

pipe.predict(['Go out for drinks'])

def evaluate_pipe(pipe, X_train, X_test):
    y_preds_train = pipe.predict(X_train)
    y_preds_test = pipe.predict(X_test)

    print('Training Accuracy:', accuracy_score(y_train, y_preds_train))
    print('Testing Accuracy:', accuracy_score(y_test, y_preds_test))
    print('\n---------------\n')
    print('Training F1:', f1_score(y_train, y_preds_train, average='weighted'))
    print('Testing F1:', f1_score(y_test, y_preds_test, average='weighted'))
    print('\n---------------\n')
    print('Train Confusion Matrix\n')
    plot_conf_matrix(y_train, y_preds_train)
    print('Test Confusion Matrix\n')
    plot_conf_matrix(y_test, y_preds_test)
    print('\n----------------\n')
    print(classification_report(y_test, y_preds_test))

evaluate_pipe(pipe, X_train, X_test)

# Save the Pipeline
joblib.dump(pipe, '../final_pipeline')

# Load pipe from joblib file to test
best_model_pipe = joblib.load('../final_pipeline')
best_model_pipe

best_model_pipe.predict(['I want to visit art galleries'])

rf_pipe = Pipeline(steps=[
                ('TextPreprocessor', PreprocessText()),
                ('TFIDFVectorizer', TfidfVectorizer(analyzer='word',
                                                    stop_words=new_stopwords,
                                                    decode_error='ignore')),
                ('DenseTransformer', DenseTransformer()),
                ('RandomForest', RandomForestClassifier(class_weight=weights_dict))])

# Use a grid search to do some model tuning with the Random Forest (iter. 6)
param_grid = {'RandomForest__n_estimators': [100, 250, 500, 750],
              'RandomForest__max_depth': [5, 7, 9]}

rf_gridsearch = GridSearchCV(rf_pipe, param_grid=param_grid,
                             verbose=1, scoring='accuracy')
rf_gridsearch.fit(X_train, y_train)

# See the params from the best model
rf_gridsearch.best_params_

# Save best estimator in a variable and get accuracy score
best_rf = rf_gridsearch.best_estimator_
y_test_preds = best_rf.predict(X_test)
accuracy_score(y_test, y_test_preds)

# Compare against training accuracy
y_train_preds = best_rf.predict(X_train)
accuracy_score(y_train, y_train_preds)

probas = best_model_pipe.predict_proba(['I want to visit art galleries'])
probas

classes = best_model_pipe.classes_
classes

# First Prediction
classes[probas.argmax()]

# Second Prediction
classes[np.argsort(probas)[:, 10]][0]

	Attraction	City	cleaned
0	SEA LIFE London Aquarium Admission Ticket	London, United Kingdom	sea life london aquarium admission ticket
1	The Jack The Ripper Walking Tour in London	London, United Kingdom	the jack the ripper walking tour in london
2	Ghost Bus Tour of London	London, United Kingdom	ghost bus tour of london
3	Big Bus London Hop-On Hop-Off Tour and River C...	London, United Kingdom	big bus london hop-on hop-off tour and river c...
4	The Blood and Tears Walk: Serial Killers and L...	London, United Kingdom	the blood and tears walk: serial killers and l...

	Attraction	City	cleaned
0	SEA LIFE London Aquarium Admission Ticket	London, United Kingdom	sea life london aquarium admission ticket
1	The Jack The Ripper Walking Tour in London	London, United Kingdom	the jack the ripper walking tour in london
2	Ghost Bus Tour of London	London, United Kingdom	ghost bus tour of london
3	Big Bus London Hop-On Hop-Off Tour and River C...	London, United Kingdom	big bus london hopon hopoff tour and river cru...
4	The Blood and Tears Walk: Serial Killers and L...	London, United Kingdom	the blood and tears walk serial killers and lo...

	Attraction	City	cleaned
0	SEA LIFE London Aquarium Admission Ticket	London, United Kingdom	sea life london aquarium admission ticket
1	The Jack The Ripper Walking Tour in London	London, United Kingdom	the jack the ripper walking tour in london
2	Ghost Bus Tour of London	London, United Kingdom	ghost bus tour of london
3	Big Bus London Hop-On Hop-Off Tour and River C...	London, United Kingdom	big bus london hopon hopoff tour and river cru...
4	The Blood and Tears Walk: Serial Killers and L...	London, United Kingdom	the blood and tears walk serial killers and lo...
5	London Ghost and Infamous Murders Walking Tour	London, United Kingdom	london ghost and infamous murders walking tour
6	Stonehenge, Windsor Castle, and Bath from London	London, United Kingdom	stonehenge windsor castle and bath from london
7	Warner Bros. Studio: The Making of Harry Potte...	London, United Kingdom	warner bros studio the making of harry potter ...
8	Ghosts, Ghouls & Gallows: London Virtual Tour	London, United Kingdom	ghosts ghouls gallows london virtual tour
9	High-Speed Thames River RIB Cruise in London	London, United Kingdom	highspeed thames river rib cruise in london

	Attraction	City	cleaned	lemmatized
0	SEA LIFE London Aquarium Admission Ticket	London, United Kingdom	sea life london aquarium admission ticket	sea life london aquarium admission ticket
1	The Jack The Ripper Walking Tour in London	London, United Kingdom	the jack the ripper walking tour in london	jack ripper walking tour london
2	Ghost Bus Tour of London	London, United Kingdom	ghost bus tour of london	ghost bus tour london
3	Big Bus London Hop-On Hop-Off Tour and River C...	London, United Kingdom	big bus london hopon hopoff tour and river cru...	big bus london hopon hopoff tour river cruise ...
4	The Blood and Tears Walk: Serial Killers and L...	London, United Kingdom	the blood and tears walk serial killers and lo...	blood tear walk serial killer london horror
5	London Ghost and Infamous Murders Walking Tour	London, United Kingdom	london ghost and infamous murders walking tour	london ghost infamous murder walk tour
6	Stonehenge, Windsor Castle, and Bath from London	London, United Kingdom	stonehenge windsor castle and bath from london	stonehenge windsor castle bath london
7	Warner Bros. Studio: The Making of Harry Potte...	London, United Kingdom	warner bros studio the making of harry potter ...	warner bros studio making harry potter luxury ...
8	Ghosts, Ghouls & Gallows: London Virtual Tour	London, United Kingdom	ghosts ghouls gallows london virtual tour	ghosts ghoul gallow london virtual tour
9	High-Speed Thames River RIB Cruise in London	London, United Kingdom	highspeed thames river rib cruise in london	highspeed thames river rib cruise london

	aal	abandon	abant	abba	abbate	abbey	abbeyprivate	abbeyst	aberfan	abian	...	تشيتا	خصوصي	دي	روما	فورميا	كاستيلوا	مدينة	من	ميرتيتوا	نقل
City
Bali, Indonesia	0.000000	0.000699	0.000000	0.0000	0.000000	0.000000	0.0000	0.000000	0.0000	0.000407	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Barcelona, Spain	0.000737	0.000000	0.000000	0.0000	0.000000	0.000000	0.0000	0.000000	0.0000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Crete, Greece	0.000000	0.000000	0.000000	0.0000	0.000000	0.000000	0.0000	0.000000	0.0000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Dubai, United Arab Emirates	0.000000	0.000309	0.000000	0.0000	0.000000	0.000000	0.0000	0.000000	0.0000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Goa, India	0.000000	0.000000	0.000000	0.0000	0.000000	0.000000	0.0000	0.000000	0.0000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Istanbul, Turkey	0.000000	0.000000	0.000589	0.0000	0.000000	0.000000	0.0000	0.000000	0.0000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
London, United Kingdom	0.000000	0.000000	0.000000	0.0005	0.000000	0.023151	0.0005	0.001001	0.0005	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Majorca, Balearic Islands	0.000000	0.000000	0.000000	0.0000	0.003344	0.000000	0.0000	0.000000	0.0000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Paris, France	0.000000	0.000000	0.000000	0.0000	0.000000	0.002363	0.0000	0.000000	0.0000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Phuket, Thailand	0.000000	0.000000	0.000000	0.0000	0.000000	0.000000	0.0000	0.000000	0.0000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Rome, Italy	0.000000	0.000000	0.000000	0.0000	0.000000	0.000944	0.0000	0.000000	0.0000	0.000000	...	0.000415	0.001244	0.000415	0.001244	0.000415	0.000415	0.002488	0.001244	0.000415	0.001244
Sicily, Italy	0.000000	0.000000	0.000000	0.0000	0.000000	0.000000	0.0000	0.000000	0.0000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

Capstone Project¶

Notebook 2: Exploratory Data Analysis, Data Preprocessing, Modeling¶

Table of Contents

Project Overview¶

Methodology & Data Used¶

Explore/clean the data¶

Duplicates¶

Class Imbalance¶

Text Cleaning & Preprocessing & More Exploration¶

Look at different vectorization strategies¶

Word Clouds with Count Vectorization¶

Word Clouds with TF-IDF Vectorization¶

Word Clouds with Bi-Grams¶

Removing Noise from the Data¶

Make Nicer Word Clouds¶

Most Frequent Words Visualizations¶

Modeling¶

Baseline Naive Bayes Model¶

Naive Bayes Iteration 2¶

Iteration 3: What happens if I take the city names out?¶

Iteration 4: Try using Count Vectorization¶

Iteration 5: Try using Bi-Grams¶

Iteration 6: Try using a Random Forest Model¶

Try out iteration 3 without lemmatization¶

Test out the model¶

Make this process into a pipeline¶

Make Pipeline and Gridsearch for Random Forest¶

Get top 2 predictions from best model¶

Conclusion¶

Model Fit & Score¶

Business Recommendations¶

Next Steps -- Dash App¶

	Attraction	City
count	28379	28379
unique	27466	12
top	Desert Safari Dubai	Bali, Indonesia
freq	15	5000

	Attraction	City
185	Windsor Castle, Stonehenge, and Oxford Day Tri...	London, United Kingdom
241	Jack the Ripper Walking Tour in London	London, United Kingdom
259	British Museum Guided Tour	London, United Kingdom
618	Private Transfer from Heathrow Airport to London	London, United Kingdom
704	Oxford City Full-Day Private Tour from London	London, United Kingdom
705	Bath and Stonehenge Full-Day Private Tour from...	London, United Kingdom
706	Full-Day Private Guided Tour of Cambridge	London, United Kingdom
716	Full-Day Private Tour of Brighton	London, United Kingdom
900	PRIVATE Jack the Ripper Ghost Walking Tour in ...	London, United Kingdom
990	London to Southampton Cruise Terminals Private...	London, United Kingdom
1074	Liverpool the Beatles Legend Fab Four and Manc...	London, United Kingdom
1365	Private Full-Day Tour of Shakespeare's Stratfo...	London, United Kingdom
1367	Bournemouth and Durdle Door Jurassic Full Day ...	London, United Kingdom
1369	Full-Day Private Tour to the Historic Naval Ci...	London, United Kingdom
1468	Full-Day Private Fun Cultural Guided Tour of L...	London, United Kingdom
1469	London Royal's Full Day Tour	London, United Kingdom
1472	Changing of the Guard Half-Day Private Walking...	London, United Kingdom
1473	3-Hour Guided Tour of Science Museum in London	London, United Kingdom
1493	London's City Lights by Night Private Tour	London, United Kingdom
1494	Theme Parks of London Chessington Full-Day Pri...	London, United Kingdom
1502	J.R.R. Tolkien's Oxford and Stonehenge Private...	London, United Kingdom
1504	Private Layover Tour from London City Airport	London, United Kingdom
1516	London Full-Day Private Shore Excursion from S...	London, United Kingdom
1517	2-Day Private Wales Tour to Cardiff and Aberfa...	London, United Kingdom
1522	London Full Day Private Tour by Walking and Pu...	London, United Kingdom
1536	Oxford City and Cotswolds Private Tour	London, United Kingdom
1537	Salisbury Magna Carta Stonehenge and Bath Priv...	London, United Kingdom
1542	The Golden Triangle Tour \| London-Oxford-Cambr...	London, United Kingdom
1606	London Skyline Tour	London, United Kingdom
1612	Wimbledon Tennis and Museum Tour	London, United Kingdom
1613	London Shopping Experience Tour	London, United Kingdom
1622	Freestyle Football Workshop in England	London, United Kingdom
1675	The Crown Netflix TV London Half Day Private Tour	London, United Kingdom
1679	007 James Bond's London Private Half Day Tour	London, United Kingdom
1732	4 Hour Tour Harry Potter Locations In London (...	London, United Kingdom
1812	Private Chauffeured Minivan at Your Disposal i...	London, United Kingdom
1846	Canterbury Cathedral and Leeds Castle Private ...	London, United Kingdom
1881	Windsor Castle Heathrow Airport Private Layover	London, United Kingdom
1882	Young Victoria's London: Windsor Castle & Kens...	London, United Kingdom
1920	9Hr Tour London Eye, Westminster Abbey and St ...	London, United Kingdom
1930	Essential London Full-Day Private Tour by Publ...	London, United Kingdom
1952	Heathrow Airport Transfer	London, United Kingdom
1961	Sherlock Holmes Walking Tour in London	London, United Kingdom
1974	Royal London Walking Tour	London, United Kingdom
2074	1066 Battle of Hastings, Birling Gap and Seven...	London, United Kingdom
2120	Full Day Traditional Private London Tour by Wa...	London, United Kingdom
2140	Zoom online tour of London	London, United Kingdom
2230	London Underground 2-Hour Tube Tour	London, United Kingdom
2282	London to Southampton Cruise Terminals Private...	London, United Kingdom
2285	Departure Private Transfers from London City t...	London, United Kingdom
2291	4 Hour Tour Tower of London and St Pauls Cathe...	London, United Kingdom
2304	Warner Bros' Making of Harry Potter Studio Tour	London, United Kingdom
2342	Arrival Private Transfers from London Railway ...	London, United Kingdom
2344	Beautiful Cornwall Two Days Private Tour	London, United Kingdom
2350	Jack the Ripper Mystery Walks	London, United Kingdom
2562	4 Hour Tour London Highlights with Private To...	London, United Kingdom
2618	The London Landmarks	London, United Kingdom
2735	Afternoon tea bus tour in London	London, United Kingdom
2772	Full Day London Pick & Mix Customized Tour	London, United Kingdom
2773	A Day at the Museum - Natural History Museum L...	London, United Kingdom

	City	Attraction
0	Bali, Indonesia	4896
10	Rome, Italy	4818
3	Dubai, United Arab Emirates	3403
6	London, United Kingdom	2743
8	Paris, France	2669
5	Istanbul, Turkey	2237
11	Sicily, Italy	1982
1	Barcelona, Spain	1831
9	Phuket, Thailand	1136
2	Crete, Greece	1019
7	Majorca, Balearic Islands	454
4	Goa, India	345

	lemmatized
City
Bali, Indonesia	hotel hotelbali private transfer daytime bali ...
Barcelona, Spain	interactive spanish cooking experience barcelo...
Crete, Greece	minoans world museum cinema crete wine ol...
Dubai, United Arab Emirates	premium red dune camel safari bbq al khayma ...
Goa, India	fontainhas heritage walk sunset cruise paradis...
Istanbul, Turkey	bosphorus sunset cruise luxury yacht istanbu...
London, United Kingdom	sea life london aquarium admission ticket jack...
Majorca, Balearic Islands	cave genova admission palma de mallorca shore ...
Paris, France	bateaux parisien seine river gourmet dinner ...
Phuket, Thailand	phi phi maiton khai island speedboat phi phi...
Rome, Italy	fast skiptheline vatican sistine chapel st pet...
Sicily, Italy	etna taormina fullday tour catania palermo str...

	Bali, Indonesia	Barcelona, Spain	Crete, Greece	Dubai, United Arab Emirates	Goa, India	Istanbul, Turkey	London, United Kingdom	Majorca, Balearic Islands	Paris, France	Phuket, Thailand	Rome, Italy	Sicily, Italy
0	(bali, 2206)	(barcelona, 1105)	(private, 340)	(dubai, 2144)	(goa, 221)	(istanbul, 1340)	(london, 1860)	(mallorca, 209)	(paris, 1653)	(phuket, 586)	(tour, 2616)	(tour, 759)
1	(tour, 2190)	(tour, 861)	(tour, 272)	(tour, 1321)	(tour, 150)	(tour, 1243)	(tour, 1463)	(tour, 144)	(tour, 1152)	(tour, 398)	(rome, 2611)	(private, 554)
2	(private, 1025)	(private, 616)	(transfer, 259)	(desert, 900)	(private, 45)	(day, 668)	(private, 1201)	(palma, 134)	(private, 949)	(island, 316)	(private, 1630)	(palermo, 422)
3	(ubud, 869)	(transfer, 172)	(airport, 251)	(safari, 898)	(day, 39)	(private, 578)	(airport, 439)	(private, 97)	(transfer, 297)	(phi, 310)	(colosseum, 656)	(transfer, 317)
4	(day, 669)	(airport, 152)	(crete, 216)	(private, 708)	(guide, 34)	(airport, 301)	(transfer, 427)	(de, 90)	(airport, 295)	(day, 161)	(vatican, 649)	(etna, 312)